Merge pull request #2 from PaddlePaddle/develop

oo

.pre-commit-config.yaml

--   repo: local
+-   repo: https://github.com/PaddlePaddle/mirrors-yapf.git
+    sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
     hooks:
     -   id: yapf
-        name: yapf
-        entry: yapf
-        language: system
-        args: [-i, --style .style.yapf]
         files: \.py$
-
 -   repo: https://github.com/pre-commit/pre-commit-hooks
     sha: a11d9314b22d8f8c7556443875b731ef05965464
     hooks:
@@ -18,6 +14,7 @@
     -   id: check-symlinks
     -   id: check-added-large-files
 -   repo: local
+
     hooks:
     -   id: copyright_checker
         name: copyright_checker

.travis.yml

 language: python
 
 python:
-  - '2.7'
   - '3.5'
-  - '3.6'
 
 script:
   - if [[ $TRAVIS_PYTHON_VERSION != 2.7 ]]; then /bin/bash ./tools/check_code_style.sh; fi

README.md

@@ -44,10 +44,15 @@ x2paddle --framework=caffe --prototxt=deploy.prototxt --weight=deploy.caffemodel
 ```
 x2paddle --framework=onnx --model=onnx_model.onnx --save_dir=pd_model
 ```
+### Paddle2ONNX
+```
+# 注意：paddle_infer_model_dir下需包含__model__和__params__两个文件
+x2paddle --framework=paddle2onnx --model=paddle_infer_model_dir --save_dir=onnx_model
+```
 ### 参数选项
 | 参数 | |
 |----------|--------------|
-|--framework | 源模型类型 (tensorflow、caffe、onnx) |
+|--framework | 源模型类型 (tensorflow、caffe、onnx、paddle2onnx) |
 |--prototxt | 当framework为caffe时，该参数指定caffe模型的proto文件路径 |
 |--weight | 当framework为caffe时，该参数指定caffe模型的参数文件路径 |
 |--save_dir | 指定转换后的模型保存目录路径 |

setup.py

@@ -11,8 +11,7 @@ setuptools.setup(
     version=x2paddle.__version__,
     author="dltp-sz",
     author_email="dltp-sz@baidu.com",
-    description=
-    "a toolkit for converting trained model to PaddlePaddle from other deep learning frameworks.",
+    description="a toolkit for converting trained model to PaddlePaddle from other deep learning frameworks.",
     long_description=long_description,
     long_description_content_type="text/plain",
     url="https://github.com/PaddlePaddle/x2paddle",
@@ -23,6 +22,4 @@ setuptools.setup(
         "Operating System :: OS Independent",
     ],
     license='Apache 2.0',
-    entry_points={'console_scripts': [
-        'x2paddle=x2paddle.convert:main',
-    ]})
+    entry_points={'console_scripts': ['x2paddle=x2paddle.convert:main', ]})

tools/merge_params.py

@@ -5,10 +5,12 @@ model_dir = sys.argv[1]
 new_model_dir = sys.argv[2]
 exe = fluid.Executor(fluid.CPUPlace())
 [inference_program, feed_target_names,
- fetch_targets] = fluid.io.load_inference_model(dirname=model_dir, executor=exe)
+ fetch_targets] = fluid.io.load_inference_model(
+     dirname=model_dir, executor=exe)
 
 print(feed_target_names)
-fluid.io.save_inference_model(dirname=new_model_dir,
+fluid.io.save_inference_model(
+    dirname=new_model_dir,
     feeded_var_names=feed_target_names,
     target_vars=fetch_targets,
     executor=exe,

x2paddle/__init__.py

-__version__ = "0.7.1"
+__version__ = "0.7.4"

x2paddle/convert.py

-#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"
 # you may not use this file except in compliance with the License.
@@ -19,22 +19,26 @@ import sys
 
 def arg_parser():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--model",
+    parser.add_argument(
+        "--model",
         "-m",
         type=_text_type,
         default=None,
         help="define model file path for tensorflow or onnx")
-    parser.add_argument("--prototxt",
+    parser.add_argument(
+        "--prototxt",
         "-p",
         type=_text_type,
         default=None,
         help="prototxt file of caffe model")
-    parser.add_argument("--weight",
+    parser.add_argument(
+        "--weight",
         "-w",
         type=_text_type,
         default=None,
         help="weight file of caffe model")
-    parser.add_argument("--save_dir",
+    parser.add_argument(
+        "--save_dir",
         "-s",
         type=_text_type,
         default=None,
@@ -44,7 +48,8 @@ def arg_parser():
         "-f",
         type=_text_type,
         default=None,
-        help="define which deeplearning framework(tensorflow/caffe/onnx)")
+        help="define which deeplearning framework(tensorflow/caffe/onnx/paddle2onnx)"
+    )
     parser.add_argument(
         "--caffe_proto",
         "-c",
@@ -52,7 +57,8 @@ def arg_parser():
         default=None,
         help="optional: the .py file compiled by caffe proto file of caffe model"
     )
-    parser.add_argument("--version",
+    parser.add_argument(
+        "--version",
         "-v",
         action="store_true",
         default=False,
@@ -63,12 +69,14 @@ def arg_parser():
         action="store_true",
         default=False,
         help="tf model conversion without data format optimization")
-    parser.add_argument("--define_input_shape",
+    parser.add_argument(
+        "--define_input_shape",
         "-d",
         action="store_true",
         default=False,
         help="define input shape for tf model")
-    parser.add_argument("--params_merge",
+    parser.add_argument(
+        "--params_merge",
         "-pm",
         action="store_true",
         default=False,
@@ -117,7 +125,6 @@ def tf2paddle(model_path,
         optimizer.merge_bias()
         optimizer.optimize_sub_graph()
 
-
 #        optimizer.merge_batch_norm()
 #        optimizer.merge_prelu()
     else:
@@ -177,6 +184,14 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
     mapper.save_inference_model(save_dir, params_merge)
 
 
+def paddle2onnx(model_path, save_dir):
+    from x2paddle.decoder.paddle_decoder import PaddleDecoder
+    from x2paddle.op_mapper.paddle_op_mapper import PaddleOpMapper
+    model = PaddleDecoder(model_path, '__model__', '__params__')
+    mapper = PaddleOpMapper()
+    mapper.convert(model.program, save_dir)
+
+
 def main():
     if len(sys.argv) < 2:
         print("Use \"x2paddle -h\" to print the help information")
@@ -249,8 +264,14 @@ def main():
         if args.params_merge:
             params_merge = True
         onnx2paddle(args.model, args.save_dir, params_merge)
+
+    elif args.framework == "paddle2onnx":
+        assert args.model is not None, "--model should be defined while translating paddle model to onnx"
+        paddle2onnx(args.model, args.save_dir)
+
     else:
-        raise Exception("--framework only support tensorflow/caffe/onnx now")
+        raise Exception(
+            "--framework only support tensorflow/caffe/onnx/paddle2onnx now")
 
 
 if __name__ == "__main__":

x2paddle/core/fluid_code.py

@@ -46,8 +46,9 @@ class Layer(object):
             for input in self.inputs:
                 if isinstance(input, GraphNode):
                     if hasattr(input, "index"):
-                        in_list += (input.layer_name +
-                                    "[{}]".format(input.index) + ", ")
+                        in_list += (
+                            input.layer_name + "[{}]".format(input.index) + ", "
+                        )
                     else:
                         in_list += (input.layer_name + ", ")
                 elif isinstance(input, six.string_types):
@@ -71,8 +72,8 @@ class Layer(object):
                     layer_code = layer_code + key + "={}, ".format(input)
         elif isinstance(self.inputs, GraphNode):
             if hasattr(self.inputs, "index"):
-                layer_code += (self.inputs.layer_name +
-                               "[{}]".format(self.inputs.index))
+                layer_code += (
+                    self.inputs.layer_name + "[{}]".format(self.inputs.index))
             else:
                 layer_code += (self.inputs.layer_name)
             if self.op != "=":
@@ -88,6 +89,8 @@ class Layer(object):
         for key, value in param_attr.items():
             if '\n' in str(value):
                 value = string(str(value).replace('\n', ','))
+            if str(key) == 'attr':
+                value = 'ParamAttr(' + str(value) + ')'
             layer_code = layer_code + key + "={}, ".format(value)
         layer_code = layer_code.strip(", ")
 

x2paddle/core/op_mapper.py

@@ -64,10 +64,8 @@ def run_net(param_dir="./"):
         b = os.path.exists(os.path.join(param_dir, var.name))
         return b
 
-    fluid.io.load_vars(exe,
-                       param_dir,
-                       fluid.default_main_program(),
-                       predicate=if_exist)
+    fluid.io.load_vars(
+        exe, param_dir, fluid.default_main_program(), predicate=if_exist)
 
 
 class OpMapper(object):
@@ -98,8 +96,8 @@ class OpMapper(object):
     def add_codes(self, codes, indent=0):
         if isinstance(codes, list):
             for code in codes:
-                self.paddle_codes += (self.tab * indent + code.strip('\n') +
-                                      '\n')
+                self.paddle_codes += (
+                    self.tab * indent + code.strip('\n') + '\n')
         elif isinstance(codes, str):
             self.paddle_codes += (self.tab * indent + codes.strip('\n') + '\n')
         else:
@@ -135,20 +133,21 @@ class OpMapper(object):
                     os.path.join(os.path.join(py_code_dir, var.name)))
                 return b
 
-            fluid.io.load_vars(exe,
+            fluid.io.load_vars(
+                exe,
                 py_code_dir,
                 fluid.default_main_program(),
                 predicate=if_exist)
             if params_merge:
-                fluid.io.save_inference_model(dirname=os.path.join(
-                    save_dir, "inference_model"),
+                fluid.io.save_inference_model(
+                    dirname=os.path.join(save_dir, "inference_model"),
                     feeded_var_names=input_names,
                     target_vars=outputs,
                     executor=exe,
                     params_filename="__params__")
             else:
-                fluid.io.save_inference_model(dirname=os.path.join(
-                    save_dir, "inference_model"),
+                fluid.io.save_inference_model(
+                    dirname=os.path.join(save_dir, "inference_model"),
                     feeded_var_names=input_names,
                     target_vars=outputs,
                     executor=exe,

x2paddle/decoder/caffe_decoder.py

@@ -49,13 +49,11 @@ class CaffeResolver(object):
 class CaffeGraphNode(GraphNode):
     def __init__(self, layer, type_str, layer_name=None):
         if layer_name is None:
-            super(CaffeGraphNode,
-                  self).__init__(layer,
-                                 layer.name.replace('/', '_').replace('-', '_'))
+            super(CaffeGraphNode, self).__init__(
+                layer, layer.name.replace('/', '_').replace('-', '_'))
         else:
-            super(CaffeGraphNode,
-                  self).__init__(layer,
-                                 layer_name.replace('/', '_').replace('-', '_'))
+            super(CaffeGraphNode, self).__init__(
+                layer, layer_name.replace('/', '_').replace('-', '_'))
         self.layer_type = type_str
         self.fluid_code = FluidCode()
         self.data = None
@@ -268,8 +266,8 @@ class CaffeDecoder(object):
                 c_i = blob.channels
                 h = blob.height
                 w = blob.width
-            data = np.asarray(list(blob.data),
-                              dtype=np.float32).reshape(c_o, c_i, h, w)
+            data = np.asarray(
+                list(blob.data), dtype=np.float32).reshape(c_o, c_i, h, w)
 
             transformed.append(data)
         return transformed

x2paddle/decoder/onnx_decoder.py

@@ -71,9 +71,8 @@ class ONNXGraphNode(GraphNode):
         if attr.type == onnx.AttributeProto.TENSOR:
             dtype = np.dtype(TENSOR_TYPE_TO_NP_TYPE[attr.t.data_type])
             data = attr.t.raw_data
-            value = np.frombuffer(data,
-                                  dtype=dtype,
-                                  count=(len(data) // dtype.itemsize))
+            value = np.frombuffer(
+                data, dtype=dtype, count=(len(data) // dtype.itemsize))
         elif attr.type == onnx.AttributeProto.STRING:
             value = attr.s
             value = value.decode() if isinstance(value, bytes) else value
@@ -205,9 +204,8 @@ class ONNXGraph(Graph):
                     self.node_map[name].weight = weight
                     self.node_map[name].embeded_as = []
             else:
-                self.node_map[name] = ONNXGraphDataNode(initializer,
-                                                        layer_name=name,
-                                                        is_global_input=False)
+                self.node_map[name] = ONNXGraphDataNode(
+                    initializer, layer_name=name, is_global_input=False)
                 self.node_map[name].weight = weight
                 self.node_map[name].embeded_as = []
 
@@ -494,8 +492,8 @@ class ONNXDecoder(object):
             sess = rt.InferenceSession(model_path)
             for ipt in sess.get_inputs():
                 datatype = datatype_map[ipt.type]
-                input_dict[ipt.name] = np.random.random(
-                    ipt.shape).astype(datatype)
+                input_dict[ipt.name] = np.random.random(ipt.shape).astype(
+                    datatype)
 
             res = sess.run(None, input_feed=input_dict)
         except:

x2paddle/decoder/paddle_decoder.py

+#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+
+
+class PaddleDecoder(object):
+    def __init__(self,
+                 model_dir,
+                 model_filename='__model__',
+                 params_filename=None):
+        exe = fluid.Executor(fluid.CPUPlace())
+        [self.program, feed, fetchs] = fluid.io.load_inference_model(
+            model_dir,
+            exe,
+            model_filename=model_filename,
+            params_filename=params_filename)

x2paddle/decoder/tf_decoder.py

@@ -120,13 +120,13 @@ class TFGraph(Graph):
     def build(self):
         for layer in self.model.node:
             self.node_map[layer.name.replace('/', '_').replace(
-                '-', '_')] = TFGraphNode(layer, data_format=self.tf_data_format)
+                '-', '_')] = TFGraphNode(
+                    layer, data_format=self.tf_data_format)
 
         for layer_name, node in self.node_map.items():
             for in_node in node.layer.input:
-                in_node = in_node.replace('/',
-                                          '_').replace('-',
-                                                       '_').replace('^', '')
+                in_node = in_node.replace('/', '_').replace('-', '_').replace(
+                    '^', '')
                 if in_node not in self.node_map:
                     if in_node.strip().split(':')[0] in self.node_map:
                         self.connect(in_node.strip().split(':')[0], layer_name)
@@ -390,10 +390,10 @@ class TFDecoder(object):
                         shape=shape,
                         name="x2paddle_{}".format(layer.name))
                 except:
-                    x2paddle_input = tf.placeholder(dtype=dtype,
+                    x2paddle_input = tf.placeholder(
+                        dtype=dtype,
                         shape=shape,
-                                                    name="x2paddle_{}".format(
-                                                        layer.name))
+                        name="x2paddle_{}".format(layer.name))
 
                 input_map["{}:0".format(layer.name)] = x2paddle_input
                 if shape.count(None) > 0:

x2paddle/op_mapper/caffe_custom_layer/convolutiondepthwise.py

@@ -122,7 +122,8 @@ def convolutiondepthwise_layer(inputs,
     c_out = num_output if num_output is not None else input_shape[0][1]
     group = int(c_in / (c_in / c_out)) if c_in > c_out else int(c_in /
                                                                 (c_out / c_in))
-    out = fluid.layers.conv2d(input,
+    out = fluid.layers.conv2d(
+        input,
         dilation=[dila_h, dila_w],
         filter_size=[k_h, k_w],
         stride=[s_h, s_w],
@@ -142,7 +143,8 @@ def convolutiondepthwise_weights(name, data=None):
     return weights_name
 
 
-register(kind='ConvolutionDepthwise',
+register(
+    kind='ConvolutionDepthwise',
     shape=convolutiondepthwise_shape,
     layer=convolutiondepthwise_layer,
     weights=convolutiondepthwise_weights)

x2paddle/op_mapper/caffe_custom_layer/detectionoutput.py

@@ -37,8 +37,8 @@ def detectionoutput_layer(inputs,
     pbv = fluid.layers.reshape(x=pbv, shape=[-1, 4])
     mbox_loc = inputs[0]
     mbox_loc = fluid.layers.reshape(x=mbox_loc, shape=[-1, pb.shape[0], 4])
-    mbox_conf_flatten = fluid.layers.reshape(x=mbox_conf_flatten,
-                                             shape=[0, pb.shape[0], -1])
+    mbox_conf_flatten = fluid.layers.reshape(
+        x=mbox_conf_flatten, shape=[0, pb.shape[0], -1])
 
     default = {"nms_threshold": 0.3, "top_k": 10, "eta": 1.0}
     fields = ['eta', 'top_k', 'nms_threshold']
@@ -64,7 +64,8 @@ def detectionoutput_weights(name, data=None):
     return weights_name
 
 
-register(kind='DetectionOutput',
+register(
+    kind='DetectionOutput',
     shape=detectionoutput_shape,
     layer=detectionoutput_layer,
     weights=detectionoutput_weights)

x2paddle/op_mapper/caffe_custom_layer/normalize.py

@@ -20,9 +20,8 @@ def normalize_layer(inputs,
         attr=name + '_scale')
     scale_param = fluid.layers.reshape(x=scale_param, \
                   shape=[1] if channel_shared else [input_shape[0][1]])
-    out = fluid.layers.elementwise_mul(x=l2_norm,
-                                       y=scale_param,
-                                       axis=-1 if channel_shared else 1)
+    out = fluid.layers.elementwise_mul(
+        x=l2_norm, y=scale_param, axis=-1 if channel_shared else 1)
     return out
 
 
@@ -31,7 +30,8 @@ def normalize_weights(name, data=None):
     return weights_name
 
 
-register(kind='Normalize',
+register(
+    kind='Normalize',
     shape=normalize_shape,
     layer=normalize_layer,
     weights=normalize_weights)

x2paddle/op_mapper/caffe_custom_layer/permute.py

@@ -23,7 +23,8 @@ def permute_weights(name, data=None):
     return weights_name
 
 
-register(kind='Permute',
+register(
+    kind='Permute',
     shape=permute_shape,
     layer=permute_layer,
     weights=permute_weights)

x2paddle/op_mapper/caffe_custom_layer/priorbox.py

@@ -30,7 +30,8 @@ def priorbox_layer(inputs,
     steps = tuple(step) if type(step) is list or type(step) is tuple else (step,
                                                                            step)
 
-    box, variance_ = fluid.layers.prior_box(input,
+    box, variance_ = fluid.layers.prior_box(
+        input,
         image,
         min_sizes=min_size,
         max_sizes=max_size,
@@ -53,7 +54,8 @@ def priorbox_weights(name, data=None):
     return weights_name
 
 
-register(kind='PriorBox',
+register(
+    kind='PriorBox',
     shape=priorbox_shape,
     layer=priorbox_layer,
     weights=priorbox_weights)

x2paddle/op_mapper/caffe_custom_layer/register.py

@@ -23,8 +23,7 @@ def register(kind, shape, layer, weights):
         kind = [kind]
     else:
         assert type(
-            kind
-        ) is list, 'invalid param "kind" for register, not a list or str'
+            kind) is list, 'invalid param "kind" for register, not a list or str'
 
     for k in kind:
         assert type(

x2paddle/op_mapper/caffe_custom_layer/roipooling.py

@@ -21,7 +21,8 @@ def roipooling_layer(inputs,
     input = inputs[0]
     roi = inputs[1]
     roi = fluid.layers.slice(roi, axes=[1], starts=[1], ends=[5])
-    out = fluid.layers.roi_pool(input,
+    out = fluid.layers.roi_pool(
+        input,
         roi,
         pooled_height=pooled_h,
         pooled_width=pooled_w,
@@ -34,7 +35,8 @@ def roipooling_weights(name, data=None):
     return weights_name
 
 
-register(kind='ROIPooling',
+register(
+    kind='ROIPooling',
     shape=roipooling_shape,
     layer=roipooling_layer,
     weights=roipooling_weights)

x2paddle/op_mapper/caffe_custom_layer/select.py

@@ -30,7 +30,8 @@ def select_layer(inputs,
     out = []
     for i in range(len(slice_point)):
         out.append(
-            fluid.layers.slice(input,
+            fluid.layers.slice(
+                input,
                 axes=[axis],
                 starts=[slice_point[i]],
                 ends=[slice_point[i + 1]],
@@ -45,7 +46,8 @@ def select_weights(name, data=None):
     return weights_name
 
 
-register(kind='Select',
+register(
+    kind='Select',
     shape=select_shape,
     layer=select_layer,
     weights=select_weights)

x2paddle/op_mapper/caffe_custom_layer/shufflechannel.py

@@ -17,7 +17,8 @@ def shufflechannel_weights(name, data=None):
     return weights_name
 
 
-register(kind='ShuffleChannel',
+register(
+    kind='ShuffleChannel',
     shape=shufflechannel_shape,
     layer=shufflechannel_layer,
     weights=shufflechannel_weights)

x2paddle/op_mapper/caffe_op_mapper.py

@@ -144,8 +144,8 @@ class CaffeOpMapper(OpMapper):
             [s_h, s_w] = [params.stride] * 2
         elif len(params.stride) > 0:
             s_h = params.stride_h if params.stride_h > 0 else params.stride[0]
-            s_w = params.stride_w if params.stride_w > 0 else params.stride[
-                len(params.stride) - 1]
+            s_w = params.stride_w if params.stride_w > 0 else params.stride[len(
+                params.stride) - 1]
         elif params.stride_h > 0 or params.stride_w > 0:
             s_h = params.stride_h
             s_w = params.stride_w
@@ -154,8 +154,8 @@ class CaffeOpMapper(OpMapper):
             [p_h, p_w] = [params.pad] * 2
         elif len(params.pad) > 0:
             p_h = params.pad_h if params.pad_h > 0 else params.pad[0]
-            p_w = params.pad_w if params.pad_w > 0 else params.pad[
-                len(params.pad) - 1]
+            p_w = params.pad_w if params.pad_w > 0 else params.pad[len(
+                params.pad) - 1]
         elif params.pad_h > 0 or params.pad_w > 0:
             p_h = params.pad_h
             p_w = params.pad_w
@@ -195,10 +195,8 @@ class CaffeOpMapper(OpMapper):
             'shape': shape,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer("data",
-                                  inputs=None,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "data", inputs=None, output=node, param_attr=attr)
 
     def MemoryData(self, node):
         # TODO(syf): Paddlepaddle can't fully support
@@ -209,10 +207,8 @@ class CaffeOpMapper(OpMapper):
             'shape': shape,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer("data",
-                                  inputs=None,
-                                  output=node.layer_name + '0',
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "data", inputs=None, output=node.layer_name + '0', param_attr=attr)
         node.fluid_code.add_note('{} = [{}]'.format(node.layer_name,
                                                     node.layer_name + '0'))
 
@@ -229,11 +225,9 @@ class CaffeOpMapper(OpMapper):
             input_c = node.input_shape[0][1]
             output_c = channel
             data.append(
-                np.zeros([output_c, input_c, kernel[0],
-                          kernel[1]]).astype('float32'))
-            data.append(np.zeros([
-                output_c,
-            ])).astype('float32')
+                np.zeros([output_c, input_c, kernel[0], kernel[1]]).astype(
+                    'float32'))
+            data.append(np.zeros([output_c, ])).astype('float32')
         else:
             data = self.adjust_parameters(node)
         self.weights[node.layer_name + '_weights'] = data[0]
@@ -244,29 +238,19 @@ class CaffeOpMapper(OpMapper):
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
 
         attr = {
-            'filter_size':
-            kernel,
-            'num_filters':
-            channel,
-            'stride':
-            stride,
-            'padding':
-            pad,
-            'dilation':
-            dilation,
-            'groups':
-            group,
-            'name':
-            string(node.layer_name),
-            'param_attr':
-            string(node.layer_name + '_weights'),
-            'bias_attr':
-            False if len(data) == 1 else string(node.layer_name + '_bias'),
+            'filter_size': kernel,
+            'num_filters': channel,
+            'stride': stride,
+            'padding': pad,
+            'dilation': dilation,
+            'groups': group,
+            'name': string(node.layer_name),
+            'param_attr': string(node.layer_name + '_weights'),
+            'bias_attr': False
+            if len(data) == 1 else string(node.layer_name + '_bias'),
         }
-        node.fluid_code.add_layer("conv2d",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "conv2d", inputs=input, output=node, param_attr=attr)
 
     def Deconvolution(self, node):
         data = node.data
@@ -281,11 +265,9 @@ class CaffeOpMapper(OpMapper):
             input_c = node.input_shape[0][1]
             output_c = channel
             data.append(
-                np.zeros([output_c, input_c, kernel[0],
-                          kernel[1]]).astype('float32'))
-            data.append(np.zeros([
-                output_c,
-            ]).astype('float32'))
+                np.zeros([output_c, input_c, kernel[0], kernel[1]]).astype(
+                    'float32'))
+            data.append(np.zeros([output_c, ]).astype('float32'))
         else:
             data = self.adjust_parameters(node)
         self.weights[node.layer_name + '_weights'] = data[0]
@@ -295,31 +277,20 @@ class CaffeOpMapper(OpMapper):
                    ) == 1, 'The count of Deconvolution node\'s input is not 1.'
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
         attr = {
-            'output_size':
-            None,
-            'filter_size':
-            kernel,
-            'num_filters':
-            channel,
-            'stride':
-            stride,
-            'padding':
-            pad,
-            'dilation':
-            dilation,
-            'groups':
-            group,
-            'name':
-            string(node.layer_name),
-            'param_attr':
-            string(node.layer_name + '_weights'),
-            'bias_attr':
-            False if len(data) == 1 else string(node.layer_name + '_bias')
+            'output_size': None,
+            'filter_size': kernel,
+            'num_filters': channel,
+            'stride': stride,
+            'padding': pad,
+            'dilation': dilation,
+            'groups': group,
+            'name': string(node.layer_name),
+            'param_attr': string(node.layer_name + '_weights'),
+            'bias_attr': False
+            if len(data) == 1 else string(node.layer_name + '_bias')
         }
-        node.fluid_code.add_layer("conv2d_transpose",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "conv2d_transpose", inputs=input, output=node, param_attr=attr)
 
     def Pooling(self, node):
         params = node.layer.pooling_param
@@ -345,10 +316,8 @@ class CaffeOpMapper(OpMapper):
             'global_pooling': global_pool,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer("pool2d",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "pool2d", inputs=input, output=node, param_attr=attr)
 
     def LRN(self, node):
         assert len(node.inputs) == 1, 'The count of LRN node\'s input is not 1.'
@@ -368,10 +337,8 @@ class CaffeOpMapper(OpMapper):
             'beta': params.beta,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer("lrn",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "lrn", inputs=input, output=node, param_attr=attr)
 
     def InnerProduct(self, node):
         data = node.data
@@ -384,8 +351,8 @@ class CaffeOpMapper(OpMapper):
             output_c = params.num_output
             data = []
             data.append(
-                np.zeros([input_c,
-                          output_c]).astype('float32').astype('float32'))
+                np.zeros([input_c, output_c]).astype('float32').astype(
+                    'float32'))
             data.append(
                 np.zeros([output_c]).astype('float32').astype('float32'))
         else:
@@ -409,21 +376,15 @@ class CaffeOpMapper(OpMapper):
         assert params.bias_term == True
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
         attr = {
-            'size':
-            params.num_output,
-            'name':
-            string(node.layer_name),
-            'act':
-            None,
-            'param_attr':
-            string(node.layer_name + '_weights'),
-            'bias_attr':
-            False if len(data) == 1 else string(node.layer_name + '_bias')
+            'size': params.num_output,
+            'name': string(node.layer_name),
+            'act': None,
+            'param_attr': string(node.layer_name + '_weights'),
+            'bias_attr': False
+            if len(data) == 1 else string(node.layer_name + '_bias')
         }
-        node.fluid_code.add_layer("fc",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "fc", inputs=input, output=node, param_attr=attr)
 
     def Softmax(self, node):
         assert len(
@@ -435,10 +396,8 @@ class CaffeOpMapper(OpMapper):
         dims = len(shape)
         axis = axis + dims if axis < 0 else axis
         attr = {'axis': axis, 'name': string(node.layer_name + '_softmax')}
-        node.fluid_code.add_layer("softmax",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "softmax", inputs=input, output=node, param_attr=attr)
 
     def Slice(self, node):
         assert len(
@@ -459,10 +418,8 @@ class CaffeOpMapper(OpMapper):
             'dim': axis,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer("split",
-                                  inputs=input,
-                                  output=node.layer_name,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "split", inputs=input, output=node.layer_name, param_attr=attr)
 
     def Concat(self, node):
         assert len(
@@ -475,10 +432,8 @@ class CaffeOpMapper(OpMapper):
         params = node.layer.concat_param
         axis = params.axis
         attr = {'axis': axis, 'name': string(node.layer_name)}
-        node.fluid_code.add_layer("concat",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "concat", inputs=inputs, output=node, param_attr=attr)
 
     def PReLU(self, node):
         assert len(
@@ -499,10 +454,8 @@ class CaffeOpMapper(OpMapper):
             'param_attr': string(node.layer_name + '_weights'),
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer("prelu",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "prelu", inputs=input, output=node, param_attr=attr)
 
     def Accuracy(self, node):
         assert len(
@@ -526,10 +479,8 @@ class CaffeOpMapper(OpMapper):
         assert axis == 1, 'PaddlePaddle can not support the situation when the axis is not 1.'
         assert not ignore_label >= 0, 'PaddlePaddle can not support the situation when the model has ignore label.'
         attr = {'k': top_k}
-        node.fluid_code.add_layer("accuracy",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "accuracy", inputs=inputs, output=node, param_attr=attr)
 
     def Eltwise(self, node):
         assert len(
@@ -546,7 +497,8 @@ class CaffeOpMapper(OpMapper):
             inputs_dict['x'] = inputs[0]
             inputs_dict['y'] = inputs[1]
             attr = {'act': None, 'name': string(node.layer_name)}
-            node.fluid_code.add_layer("elementwise_mul",
+            node.fluid_code.add_layer(
+                "elementwise_mul",
                 inputs=inputs_dict,
                 output=node,
                 param_attr=attr)
@@ -559,14 +511,15 @@ class CaffeOpMapper(OpMapper):
                     'value': coeff[0],
                     'dtype': '{}.dtype'.format(input1_name)
                 }
-                node.fluid_code.add_layer("fill_constant",
+                node.fluid_code.add_layer(
+                    "fill_constant",
                     inputs=None,
                     output=node.layer_name + '_const1',
                     param_attr=attr)
                 attr = {'act': None, 'name': string(node.layer_name + '_mul1')}
-                node.fluid_code.add_layer("elementwise_mul",
-                                          inputs=input1_name + ', ' +
-                                          node.layer_name + '_const1',
+                node.fluid_code.add_layer(
+                    "elementwise_mul",
+                    inputs=input1_name + ', ' + node.layer_name + '_const1',
                     output=node.layer_name + '_mul1',
                     param_attr=attr)
                 input2_name = self.get_input_name(inputs[1])
@@ -575,21 +528,23 @@ class CaffeOpMapper(OpMapper):
                     'value': coeff[1],
                     'dtype': '{}.dtype'.format(input2_name)
                 }
-                node.fluid_code.add_layer("fill_constant",
+                node.fluid_code.add_layer(
+                    "fill_constant",
                     inputs=None,
                     output=node.layer_name + '_const2',
                     param_attr=attr)
                 attr = {'act': None, 'name': string(node.layer_name + '_mul2')}
-                node.fluid_code.add_layer("elementwise_mul",
-                                          inputs=input2_name + ', ' +
-                                          node.layer_name + '_const2',
+                node.fluid_code.add_layer(
+                    "elementwise_mul",
+                    inputs=input2_name + ', ' + node.layer_name + '_const2',
                     output=node.layer_name + '_mul2',
                     param_attr=attr)
 
                 attr = {'act': None, 'name': string(node.layer_name)}
-                node.fluid_code.add_layer("elementwise_add",
-                                          inputs='{}_mul1, {}_mul2'.format(
-                                              node.layer_name, node.layer_name),
+                node.fluid_code.add_layer(
+                    "elementwise_add",
+                    inputs='{}_mul1, {}_mul2'.format(node.layer_name,
+                                                     node.layer_name),
                     output=node,
                     param_attr=attr)
             else:
@@ -597,7 +552,8 @@ class CaffeOpMapper(OpMapper):
                 inputs_dict['x'] = inputs[0]
                 inputs_dict['y'] = inputs[1]
                 attr = {'act': None, 'name': string(node.layer_name)}
-                node.fluid_code.add_layer("elementwise_add",
+                node.fluid_code.add_layer(
+                    "elementwise_add",
                     inputs=inputs_dict,
                     output=node,
                     param_attr=attr)
@@ -606,7 +562,8 @@ class CaffeOpMapper(OpMapper):
             inputs_dict['x'] = inputs[0]
             inputs_dict['y'] = inputs[1]
             attr = {'act': None, 'name': string(node.layer_name)}
-            node.fluid_code.add_layer("elementwise_max",
+            node.fluid_code.add_layer(
+                "elementwise_max",
                 inputs=inputs_dict,
                 output=node,
                 param_attr=attr)
@@ -625,12 +582,8 @@ class CaffeOpMapper(OpMapper):
                 'The parameter of {} (type is {}) is not set. So we set the parameters as 0'
                 .format(node.layer_name, node.layer_type))
             input_c = node.input_shape[0][1]
-            mean = np.zeros([
-                input_c,
-            ]).astype('float32')
-            variance = np.zeros([
-                input_c,
-            ]).astype('float32')
+            mean = np.zeros([input_c, ]).astype('float32')
+            variance = np.zeros([input_c, ]).astype('float32')
             scale = 0
         else:
 
@@ -651,10 +604,8 @@ class CaffeOpMapper(OpMapper):
             'epsilon': eps,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer("batch_norm",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "batch_norm", inputs=input, output=node, param_attr=attr)
 
     def Scale(self, node):
         if node.data is None:
@@ -669,10 +620,10 @@ class CaffeOpMapper(OpMapper):
                 input_c,
             ]).astype('float32')
         else:
-            self.weights[node.layer_name + '_scale'] = np.squeeze(
-                node.data[0]).astype('float32')
-            self.weights[node.layer_name + '_offset'] = np.squeeze(
-                node.data[1]).astype('float32')
+            self.weights[node.layer_name + '_scale'] = np.squeeze(node.data[
+                0]).astype('float32')
+            self.weights[node.layer_name + '_offset'] = np.squeeze(node.data[
+                1]).astype('float32')
         params = node.layer.scale_param
         axis = params.axis
         num_axes = params.num_axes
@@ -687,7 +638,8 @@ class CaffeOpMapper(OpMapper):
             inputs_dict['x'] = input0
             inputs_dict['y'] = input1
             attr = {'axis': axis, 'name': string(node.layer_name + '_mul')}
-            node.fluid_code.add_layer("elementwise_mul",
+            node.fluid_code.add_layer(
+                "elementwise_mul",
                 inputs=inputs_dict,
                 output=node.layer_name + '_mul',
                 param_attr=attr)
@@ -703,15 +655,14 @@ class CaffeOpMapper(OpMapper):
                 'is_bias': True,
                 'default_initializer': 'Constant(value=1.0)'
             }
-            node.fluid_code.add_layer("create_parameter",
-                                      inputs=None,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "create_parameter", inputs=None, output=node, param_attr=attr)
             inputs_dict = {}
             inputs_dict['x'] = input0
             inputs_dict['y'] = node
             attr = {'axis': axis, 'name': string(node.layer_name + '_mul')}
-            node.fluid_code.add_layer("elementwise_mul",
+            node.fluid_code.add_layer(
+                "elementwise_mul",
                 inputs=inputs_dict,
                 output=node.layer_name + '_mul',
                 param_attr=attr)
@@ -725,14 +676,16 @@ class CaffeOpMapper(OpMapper):
             'is_bias': True,
             'default_initializer': 'Constant(value=1.0)'
         }
-        node.fluid_code.add_layer("create_parameter",
+        node.fluid_code.add_layer(
+            "create_parameter",
             inputs=None,
             output=node.layer_name + '_offset_param',
             param_attr=attr)
         attr = {'axis': axis, 'name': string(node.layer_name + '_add')}
-        node.fluid_code.add_layer("elementwise_add",
-                                  inputs='{}_mul, {}_offset_param'.format(
-                                      node.layer_name, node.layer_name),
+        node.fluid_code.add_layer(
+            "elementwise_add",
+            inputs='{}_mul, {}_offset_param'.format(node.layer_name,
+                                                    node.layer_name),
             output=node,
             param_attr=attr)
 
@@ -747,10 +700,8 @@ class CaffeOpMapper(OpMapper):
             'act': None,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer("reshape",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "reshape", inputs=input, output=node, param_attr=attr)
 
     def ArgMax(self, node):
         assert len(node.inputs) == 1 and len(
@@ -767,10 +718,11 @@ class CaffeOpMapper(OpMapper):
             axis += len(input_shape)
         if out_max_val is True:
             attr = {'k': top_k, 'name': string(node.layer_name + '_topk')}
-            node.fluid_code.add_layer("topk",
+            node.fluid_code.add_layer(
+                "topk",
                 inputs=input,
-                                      output='{}_topk_var, {}_index_var'.format(
-                                          node.layer_name, node.layer_name),
+                output='{}_topk_var, {}_index_var'.format(node.layer_name,
+                                                          node.layer_name),
                 param_attr=attr)
             attr = {'dtype': '{}_topk_var.dtype'.format(node.layer_name)}
             node.fluid_code.add_layer(
@@ -779,14 +731,16 @@ class CaffeOpMapper(OpMapper):
                 output='{}_index_var'.format(node.layer_name),
                 param_attr=attr)
             attr = {'axis': axis, 'name': string(node.layer_name)}
-            node.fluid_code.add_layer("concat",
-                                      inputs='{}_topk_var, {}_index_var'.format(
-                                          node.layer_name, node.layer_name),
+            node.fluid_code.add_layer(
+                "concat",
+                inputs='{}_topk_var, {}_index_var'.format(node.layer_name,
+                                                          node.layer_name),
                 output=node,
                 param_attr=attr)
         else:
             attr = {'k': top_k, 'name': string(node.layer_name)}
-            node.fluid_code.add_layer("topk",
+            node.fluid_code.add_layer(
+                "topk",
                 inputs=input,
                 output='_, {}'.format(node.layer_name),
                 param_attr=attr)
@@ -804,29 +758,27 @@ class CaffeOpMapper(OpMapper):
         offset_real = [0] * len(input_shape)
         if hasattr(params, "offset") and len(params.offset) > 0:
             offset = list(params.offset)
-            assert (len(input_shape) - axis) == len(
-                offset), "invalid offset[%s] in crop layer" % (str(offset))
+            assert (len(input_shape) - axis
+                    ) == len(offset), "invalid offset[%s] in crop layer" % (
+                        str(offset))
             offset_real = [0] * axis + offset
         attr = {'offsets': list(offset_real), 'name': string(node.layer_name)}
-        node.fluid_code.add_layer("crop",
-                                  inputs={
-                                      'x': input,
-                                      'shape': node.input_shape[1]
-                                  },
+        node.fluid_code.add_layer(
+            "crop",
+            inputs={'x': input,
+                    'shape': node.input_shape[1]},
             output=node,
             param_attr=attr)
 
     def Flatten(self, node):
         assert len(
-            node.inputs
-        ) == 1, 'The count of DetectionOutput node\'s input is not 1.'
+            node.
+            inputs) == 1, 'The count of DetectionOutput node\'s input is not 1.'
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
         shape = node.output_shape[0]
         attr = {'shape': shape, 'name': string(node.layer_name)}
-        node.fluid_code.add_layer("reshape",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "reshape", inputs=input, output=node, param_attr=attr)
 
     def Power(self, node):
         assert len(
@@ -842,15 +794,11 @@ class CaffeOpMapper(OpMapper):
             'bias_after_scale': True,
             'name': string(node.layer_name + '_scale')
         }
-        node.fluid_code.add_layer("scale",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "scale", inputs=input, output=node, param_attr=attr)
         attr = {'factor': power, 'name': string(node.layer_name)}
-        node.fluid_code.add_layer("pow",
-                                  inputs=node,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "pow", inputs=node, output=node, param_attr=attr)
 
     def Reduction(self, node):
         assert len(
@@ -872,55 +820,41 @@ class CaffeOpMapper(OpMapper):
                 'keep_dim': False,
                 'name': string(node.layer_name)
             }
-            node.fluid_code.add_layer("reduce_sum",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "reduce_sum", inputs=input, output=node, param_attr=attr)
         elif operation == 2:  ## operation = ASUM
             attr = {'name': string(node.layer_name + '_abs')}
-            node.fluid_code.add_layer("abs",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "abs", inputs=input, output=node, param_attr=attr)
             attr = {
                 'dim': dim[axis:],
                 'keep_dim': False,
                 'name': string(node.layer_name)
             }
-            node.fluid_code.add_layer("reduce_sum",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "reduce_sum", inputs=node, output=node, param_attr=attr)
         elif operation == 3:  ## operation = SUMSQ
             attr = {'factor': 2.0, 'name': string(node.layer_name + '_pow')}
-            node.fluid_code.add_layer("pow",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "pow", inputs=input, output=node, param_attr=attr)
             attr = {
                 'dim': dim[axis:],
                 'keep_dim': False,
                 'name': string(node.layer_name)
             }
-            node.fluid_code.add_layer("reduce_sum",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "reduce_sum", inputs=node, output=node, param_attr=attr)
         else:  ## operation = MEAN
             attr = {
                 'dim': dim[axis:],
                 'keep_dim': False,
                 'name': string(node.layer_name)
             }
-            node.fluid_code.add_layer("reduce_mean",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "reduce_mean", inputs=node, output=node, param_attr=attr)
         attr = {'scale': coeff}
-        node.fluid_code.add_layer("scale",
-                                  inputs=node,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "scale", inputs=node, output=node, param_attr=attr)
 
     def deal_custom_layer(self, node):
         op = node.layer_type
@@ -947,7 +881,8 @@ class CaffeOpMapper(OpMapper):
                 assert input is not None, 'This kind of DetectionOutput is not supported!'
                 input = self.graph.get_bottom_node(input, idx=0, copy=True)
             inputs_node.append(input)
-        node.fluid_code.add_layer(func.__code__.co_name,
+        node.fluid_code.add_layer(
+            func.__code__.co_name,
             inputs=inputs_node,
             output=node,
             param_attr=kwargs,
@@ -960,7 +895,5 @@ class CaffeOpMapper(OpMapper):
         op_info = self.directly_map_ops[node.layer_type]
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
         attr = {'name': string(node.layer_name)}
-        node.fluid_code.add_layer(op_info,
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            op_info, inputs=input, output=node, param_attr=attr)

x2paddle/op_mapper/caffe_shape.py

@@ -33,8 +33,8 @@ def get_kernel_parameters(params):
         [s_h, s_w] = [params.stride] * 2
     elif len(params.stride) > 0:
         s_h = params.stride_h if params.stride_h > 0 else params.stride[0]
-        s_w = params.stride_w if params.stride_w > 0 else params.stride[
-            len(params.stride) - 1]
+        s_w = params.stride_w if params.stride_w > 0 else params.stride[len(
+            params.stride) - 1]
     elif params.stride_h > 0 or params.stride_w > 0:
         s_h = params.stride_h
         s_w = params.stride_w

x2paddle/op_mapper/onnx_custom_layer/InstanceNormalization.py

@@ -24,21 +24,18 @@ def InstanceNormalization_layer(inputs, name=None):
     epsilon = 1e-5
     input_ = inputs[0]
     mean = fluid.layers.reduce_mean(input_, dim=[2, 3], keep_dim=True)
-    var = fluid.layers.reduce_mean(fluid.layers.square(input_ - mean),
-                                   dim=[2, 3],
-                                   keep_dim=True)
+    var = fluid.layers.reduce_mean(
+        fluid.layers.square(input_ - mean), dim=[2, 3], keep_dim=True)
     if name is not None:
         scale_name = name + "_scale"
         offset_name = name + "_offset"
 
     scale_param = inputs[1]
     offset_param = inputs[2]
-    scale = fluid.layers.create_parameter(name=scale_param.name,
-                                          shape=input_.shape[1:2],
-                                          dtype="float32")
-    offset = fluid.layers.create_parameter(name=offset_param.name,
-                                           shape=input_.shape[1:2],
-                                           dtype="float32")
+    scale = fluid.layers.create_parameter(
+        name=scale_param.name, shape=input_.shape[1:2], dtype="float32")
+    offset = fluid.layers.create_parameter(
+        name=offset_param.name, shape=input_.shape[1:2], dtype="float32")
 
     tmp = fluid.layers.elementwise_mul(x=(input_ - mean), y=scale, axis=1)
     tmp = tmp / fluid.layers.sqrt(var + epsilon)
@@ -51,7 +48,8 @@ def InstanceNormalization_weights(name, data=None):
     return weights_name
 
 
-register(kind='InstanceNormalization',
+register(
+    kind='InstanceNormalization',
     shape=InstanceNormalization_shape,
     layer=InstanceNormalization_layer,
     child_func=None,

x2paddle/op_mapper/onnx_custom_layer/register.py

@@ -36,8 +36,7 @@ def register(kind, shape, layer, child_func, weights):
         kind = [kind]
     else:
         assert type(
-            kind
-        ) is list, 'invalid param "kind" for register, not a list or str'
+            kind) is list, 'invalid param "kind" for register, not a list or str'
 
     for k in kind:
         assert type(

x2paddle/op_mapper/onnx_directly_map.py

@@ -28,61 +28,55 @@ default_op_mapping_field_values['FILL_NAME_FIELD'] = True
 default_op_mapping = {
     'Shape': ['shape', ['X'], ['Out']],
     'Clip': [
-        'clip', ['X'], ['Out'],
-        dict(),
-        dict(
-            min=(_np.asarray([255, 255, 127, 255],
-                             dtype=_np.uint8).view(_np.float32)[0]),
-            max=(_np.asarray([255, 255, 127, 127],
-                             dtype=_np.uint8).view(_np.float32)[0]),
-        )
+        'clip', ['X'], ['Out'], dict(), dict(
+            min=(_np.asarray(
+                [255, 255, 127, 255], dtype=_np.uint8).view(_np.float32)[0]),
+            max=(_np.asarray(
+                [255, 255, 127, 127], dtype=_np.uint8).view(_np.float32)[0]), )
     ],
     'Erf': ['erf', ['X'], ['Out']],
     'Ceil': ['ceil', ['X'], ['Out']],
     'ReduceMean': [
-        'reduce_mean', ['X'], ['Out'],
-        dict(axes='dim', keepdims='keep_dim'),
-        dict(keep_dim=1)
+        'reduce_mean', ['X'], ['Out'], dict(
+            axes='dim', keepdims='keep_dim'), dict(keep_dim=1)
     ],
     'ReduceSum': [
-        'reduce_sum', ['X'], ['Out'],
-        dict(axes='dim', keepdims='keep_dim'),
-        dict(keep_dim=1)
+        'reduce_sum', ['X'], ['Out'], dict(
+            axes='dim', keepdims='keep_dim'), dict(keep_dim=1)
     ],
     'ReduceMin': [
-        'reduce_min', ['X'], ['Out'],
-        dict(axes='dim', keepdims='keep_dim'),
-        dict(keep_dim=1)
+        'reduce_min', ['X'], ['Out'], dict(
+            axes='dim', keepdims='keep_dim'), dict(keep_dim=1)
+    ],
+    'ReduceMax': [
+        'reduce_max', ['X'], ['Out'], dict(
+            axes='dim', keepdims='keep_dim'), dict(keep_dim=1)
     ],
     #active function
     'Relu': ['relu', ['X'], ['Out']],
-    'LeakyRelu': ['leaky_relu', ['X'], ['Out'],
-                  dict(), dict(alpha=.01)],
-    'Elu': ['elu', ['X'], ['Out'],
-            dict(), dict(alpha=1.)],
+    'LeakyRelu': ['leaky_relu', ['X'], ['Out'], dict(), dict(alpha=.01)],
+    'Elu': ['elu', ['X'], ['Out'], dict(), dict(alpha=1.)],
     'ThresholdedRelu': [
-        'thresholded_relu', ['X'], ['Out'],
-        dict(alpha='threshold'),
+        'thresholded_relu', ['X'], ['Out'], dict(alpha='threshold'),
         dict(alpha=1.)
     ],
     'Tanh': ['tanh', ['X'], ['Out']],
     'Sigmoid': ['sigmoid', ['X'], ['Out']],
     'HardSigmoid': [
-        'hard_sigmoid', ['X'], ['Out'],
-        dict(alpha='slope', beta='offset'),
-        dict(slope=.2, offset=.5)
+        'hard_sigmoid', ['X'], ['Out'], dict(
+            alpha='slope', beta='offset'), dict(
+                slope=.2, offset=.5)
     ],
     'Softsign': ['softsign', ['X'], ['Out']],
     'Softplus': ['softplus', ['X'], ['Out']],
     'Exp': ['exp', ['X'], ['Out']],
-    'Softmax': ['softmax', ['X'], ['Out'],
-                dict(), dict(axis=1)],
+    'Softmax': ['softmax', ['X'], ['Out'], dict(), dict(axis=1)],
     'Sqrt': ['sqrt', ['X'], ['Out']],
     'Floor': ['floor', ['X'], ['Out']],
     'Abs': ['abs', ['X'], ['Out']],
 }
 
 default_ioa_constraint = {
-    'Gather':
-    [(lambda i, o, a: a.get('axis', 0) == 0, 'only axis = 0 is supported')],
+    'Gather': [(lambda i, o, a: a.get('axis', 0) == 0,
+                'only axis = 0 is supported')],
 }

x2paddle/op_mapper/onnx_op_mapper.py

@@ -140,8 +140,8 @@ class ONNXOpMapper(OpMapper):
 
         model.graph.ClearField('output')
         model.graph.output.MergeFrom(model.graph.value_info)
-        onnx.save(model, os.path.join(self.tmp_data_dir,
-                                      'onnx_model_infer.onnx'))
+        onnx.save(model,
+                  os.path.join(self.tmp_data_dir, 'onnx_model_infer.onnx'))
         sess = rt.InferenceSession(
             os.path.join(self.tmp_data_dir, 'onnx_model_infer.onnx'))
         res = sess.run(None, input_feed=inputs_dict)
@@ -217,8 +217,7 @@ class ONNXOpMapper(OpMapper):
             default_attrs,
             input_perm,
             output_perm,
-            fill_name_field,
-        ) = info
+            fill_name_field, ) = info
 
         if fluid_op in default_ioa_constraint:
             for predicate, message in default_ioa_constraint[fluid_op]:
@@ -246,10 +245,8 @@ class ONNXOpMapper(OpMapper):
         assert len(val_inps) == 1, 'directly_map error with multi inputs'
         if fluid_op not in ['shape']:
             attr['name'] = string(node.layer_name)
-        node.fluid_code.add_layer(fluid_op,
-                                  inputs=val_inps[0],
-                                  output=val_outs[0],
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            fluid_op, inputs=val_inps[0], output=val_outs[0], param_attr=attr)
 
     def deal_custom_layer(self, node):
         op = node.layer_type
@@ -258,7 +255,8 @@ class ONNXOpMapper(OpMapper):
         params = get_params(node.layer, node.layer_type)
         arg_names, kwargs = set_args(func, params)
         kwargs['name'] = string(node.layer_name)
-        node.fluid_code.add_layer(func.__code__.co_name,
+        node.fluid_code.add_layer(
+            func.__code__.co_name,
             inputs=node.inputs,
             output=node,
             param_attr=kwargs,
@@ -299,21 +297,18 @@ class ONNXOpMapper(OpMapper):
                 'shape': val_y_reshaped,
                 'name': string(var_y_reshaped)
             }
-            node.fluid_code.add_layer('reshape',
+            node.fluid_code.add_layer(
+                'reshape',
                 inputs=val_y,
                 output=var_y_reshaped,
                 param_attr=attr_reshaped)
             inputs = {'x': val_x, 'y': var_y_reshaped}
-            node.fluid_code.add_layer(op_type,
-                                      inputs=inputs,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                op_type, inputs=inputs, output=node, param_attr=attr)
         else:
             inputs = {'x': val_x, 'y': val_y}
-            node.fluid_code.add_layer(op_type,
-                                      inputs=inputs,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                op_type, inputs=inputs, output=node, param_attr=attr)
 
     def place_holder(self, node):
         self.input_shapes.append(node.out_shapes[0])
@@ -331,10 +326,8 @@ class ONNXOpMapper(OpMapper):
             "append_batch_size": 'False'
         }
 
-        node.fluid_code.add_layer("data",
-                                  inputs=None,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "data", inputs=None, output=node, param_attr=attr)
 
     def create_parameter(self, node, parameter=None):
         if parameter is not None:
@@ -351,10 +344,8 @@ class ONNXOpMapper(OpMapper):
             'attr': string(node.layer_name),
             'default_initializer': 'Constant(0.0)'
         }
-        node.fluid_code.add_layer("create_parameter",
-                                  inputs=None,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "create_parameter", inputs=None, output=node, param_attr=attr)
 
     def _pad_if_asymmetric(self, node, pads, val_name):  # pads: SSEE
         assert len(pads) & 1 == 0
@@ -418,10 +409,8 @@ class ONNXOpMapper(OpMapper):
         else:
             attr['out_shape'] = out_shape
 
-        node.fluid_code.add_layer(fluid_op,
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            fluid_op, inputs=val_x, output=node, param_attr=attr)
 
     def RoiAlign(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -437,11 +426,10 @@ class ONNXOpMapper(OpMapper):
             'spatial_scale': spatial_scale,
             'sampling_ratio': sampling_ratio,
         }
-        node.fluid_code.add_layer('roi_align',
-                                  inputs={
-                                      'input': val_x,
-                                      'rois': val_rois
-                                  },
+        node.fluid_code.add_layer(
+            'roi_align',
+            inputs={'input': val_x,
+                    'rois': val_rois},
             output=node,
             param_attr=attr)
 
@@ -456,11 +444,10 @@ class ONNXOpMapper(OpMapper):
             'pooled_width': pooled_width,
             'spatial_scale': spatial_scale,
         }
-        node.fluid_code.add_layer('roi_pool',
-                                  inputs={
-                                      'input': val_x,
-                                      'rois': val_rois
-                                  },
+        node.fluid_code.add_layer(
+            'roi_pool',
+            inputs={'input': val_x,
+                    'rois': val_rois},
             output=node,
             param_attr=attr)
 
@@ -499,13 +486,12 @@ class ONNXOpMapper(OpMapper):
         attr['paddings'] = paddings
         if op_independent:
             attr['name'] = string(node.layer_name)
-            node.fluid_code.add_layer(fluid_op,
-                                      inputs=val_x,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                fluid_op, inputs=val_x, output=node, param_attr=attr)
         else:
             attr['name'] = string(node.layer_name + '_paded')
-            node.fluid_code.add_layer(fluid_op,
+            node.fluid_code.add_layer(
+                fluid_op,
                 inputs=val_x,
                 output=node.layer_name + '_paded',
                 param_attr=attr)
@@ -515,16 +501,12 @@ class ONNXOpMapper(OpMapper):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         axes = node.get_attr('axes')
         if len(val_x.out_shapes[0]) == 0:
-            node.fluid_code.add_layer('assign',
-                                      inputs=val_x,
-                                      output=node,
-                                      param_attr=None)
+            node.fluid_code.add_layer(
+                'assign', inputs=val_x, output=node, param_attr=None)
         else:
             attr = {'axes': axes, 'name': string(node.layer_name)}
-            node.fluid_code.add_layer('unsqueeze',
-                                      inputs=val_x,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                'unsqueeze', inputs=val_x, output=node, param_attr=attr)
 
     def Shrink(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -532,10 +514,18 @@ class ONNXOpMapper(OpMapper):
         lambd = node.get_attr('lambd')
         assert bias == 0.0, 'not support bias!=0'
         attr = {'threshold': lambd, 'name': node.layer_name}
-        node.fluid_code.add_layer('hard_shrink',
-                                  inputs=val_x,
+        node.fluid_code.add_layer(
+            'hard_shrink', inputs=val_x, output=node, param_attr=attr)
+
+    def Greater(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_y = self.graph.get_input_node(node, idx=1, copy=True)
+        node.fluid_code.add_layer(
+            'greater_than',
+            inputs={'x': val_x,
+                    'y': val_y},
             output=node,
-                                  param_attr=attr)
+            param_attr=None)
 
     def Constant(self, node):
         val_output = self.graph.get_node(node.layer.output[0], copy=True)
@@ -552,8 +542,7 @@ class ONNXOpMapper(OpMapper):
             shape = val_output.out_shapes[0]
         if shape is None:
             shape = list(value.shape)
-            _logger.warning(
-                'in (Constant -> %s): '
+            _logger.warning('in (Constant -> %s): '
                             'attribute "shape" of %s not inferred, '
                             'using value as 1-D tensor may lead to fails',
                             val_output.layer_name, val_output.layer_name)
@@ -565,10 +554,8 @@ class ONNXOpMapper(OpMapper):
             if dtype.name == 'int64':
                 dtype = 'int32'
             attr = {'shape': shape, 'dtype': string(dtype), 'value': value}
-            node.fluid_code.add_layer('fill_constant',
-                                      inputs=None,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                'fill_constant', inputs=None, output=node, param_attr=attr)
         else:
             value = np.reshape(value, shape)
             self.weights[node.layer_name] = value
@@ -579,10 +566,8 @@ class ONNXOpMapper(OpMapper):
                 'attr': string(node.layer_name),
                 'default_initializer': 'Constant(0.0)'
             }
-            node.fluid_code.add_layer("create_parameter",
-                                      inputs=None,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "create_parameter", inputs=None, output=node, param_attr=attr)
 
     def Resize(self, node):
         self._interpolate(node)
@@ -603,13 +588,12 @@ class ONNXOpMapper(OpMapper):
 
         name_ones = node.layer_name + '_ones'
         attr_ones = {'shape': out_shape, 'dtype': string(val_x_dtype)}
-        node.fluid_code.add_layer('ones',
-                                  inputs=None,
-                                  output=name_ones,
-                                  param_attr=attr_ones)
+        node.fluid_code.add_layer(
+            'ones', inputs=None, output=name_ones, param_attr=attr_ones)
         inputs = {'x': name_ones, 'y': val_x}
         attr = {'name': string(node.layer_name)}
-        node.fluid_code.add_layer('elementwise_mul',
+        node.fluid_code.add_layer(
+            'elementwise_mul',
             inputs=inputs,
             output=node.layer_name,
             param_attr=attr)
@@ -622,11 +606,10 @@ class ONNXOpMapper(OpMapper):
         assert len(
             indices_shape) <= 2, "Gather op don't support dim of indice >2 "
         if axis == 0 and len(indices_shape) <= 1:
-            node.fluid_code.add_layer('gather',
-                                      inputs={
-                                          'input': val_x,
-                                          'index': indices
-                                      },
+            node.fluid_code.add_layer(
+                'gather',
+                inputs={'input': val_x,
+                        'index': indices},
                 output=node,
                 param_attr=None)
         elif axis > 0 and len(indices_shape) <= 1:
@@ -634,57 +617,53 @@ class ONNXOpMapper(OpMapper):
             perm = [axis] + perm[:axis] + perm[axis + 1:]
             attr_trans = {'perm': perm}
             name_trans = val_x.layer_name + '_trans'
-            node.fluid_code.add_layer('transpose',
+            node.fluid_code.add_layer(
+                'transpose',
                 inputs=val_x,
                 output=name_trans,
                 param_attr=attr_trans)
-            node.fluid_code.add_layer('gather',
-                                      inputs={
-                                          'input': name_trans,
-                                          'index': indices
-                                      },
+            node.fluid_code.add_layer(
+                'gather',
+                inputs={'input': name_trans,
+                        'index': indices},
                 output=node,
                 param_attr=None)
-            node.fluid_code.add_layer('transpose',
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr_trans)
+            node.fluid_code.add_layer(
+                'transpose', inputs=node, output=node, param_attr=attr_trans)
         elif len(indices_shape) > 1:
             from functools import reduce
             reshape_shape = reduce(lambda x, y: x * y, indices_shape)
-            node.fluid_code.add_layer('reshape',
+            node.fluid_code.add_layer(
+                'reshape',
                 inputs=indices,
                 output=indices,
-                                      param_attr={'shape': [
-                                          reshape_shape,
-                                      ]})
+                param_attr={'shape': [reshape_shape, ]})
 
             perm = list(range(len(val_x.out_shapes[0])))
             perm = [axis] + perm[:axis] + perm[axis + 1:]
             attr_trans = {'perm': perm}
             name_trans = val_x.layer_name + '_trans'
-            node.fluid_code.add_layer('transpose',
+            node.fluid_code.add_layer(
+                'transpose',
                 inputs=val_x,
                 output=name_trans,
                 param_attr=attr_trans)
-            node.fluid_code.add_layer('gather',
-                                      inputs={
-                                          'input': name_trans,
-                                          'index': indices
-                                      },
+            node.fluid_code.add_layer(
+                'gather',
+                inputs={'input': name_trans,
+                        'index': indices},
                 output=node,
                 param_attr=None)
-            node.fluid_code.add_layer('transpose',
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr_trans)
+            node.fluid_code.add_layer(
+                'transpose', inputs=node, output=node, param_attr=attr_trans)
             val_x_shape = val_x.out_shapes[0]
             reshaped_shape = []
             for i in perm:
                 reshaped_shape.append(indices_shape[i])
             for i in val_x_shape[:axis] + val_x_shape[axis + 1:]:
                 reshaped_shape.append(i)
-            node.fluid_code.add_layer('reshape',
+            node.fluid_code.add_layer(
+                'reshape',
                 inputs=node,
                 output=node,
                 param_attr={'shape': reshaped_shape})
@@ -725,10 +704,8 @@ class ONNXOpMapper(OpMapper):
                 if value > shape[axes[idx]]:
                     ends[idx] = shape[axes[idx]]
         attr = {"axes": axes, "starts": starts, "ends": ends}
-        node.fluid_code.add_layer('slice',
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            'slice', inputs=val_x, output=node, param_attr=attr)
 
     def ConstantOfShape(self, node):
         val_shape = self.graph.get_input_node(node, idx=0, copy=True)
@@ -751,10 +728,8 @@ class ONNXOpMapper(OpMapper):
             if dtype.name == 'int64':
                 dtype = 'int32'
             attr = {'shape': shape, 'dtype': string(dtype), 'value': value}
-            node.fluid_code.add_layer('fill_constant',
-                                      inputs=None,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                'fill_constant', inputs=None, output=node, param_attr=attr)
 
     def Split(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -769,10 +744,8 @@ class ONNXOpMapper(OpMapper):
             'name': string(node.layer_name)
         }
 
-        node.fluid_code.add_layer('split',
-                                  inputs=val_x,
-                                  output=val_y,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            'split', inputs=val_x, output=val_y, param_attr=attr)
 
     def Reshape(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -789,7 +762,8 @@ class ONNXOpMapper(OpMapper):
             shape, _, _ = self.get_dynamic_shape(val_shape.layer_name)
             if val_shape.dtype == 'int64':
                 val_shape_cast = val_shape.layer_name + '_cast'
-                node.fluid_code.add_layer('cast',
+                node.fluid_code.add_layer(
+                    'cast',
                     inputs=val_shape,
                     output=val_shape_cast,
                     param_attr={'dtype': string('int32')})
@@ -810,10 +784,8 @@ class ONNXOpMapper(OpMapper):
                 val_x.layer_name, val_reshaped.layer_name)
 
         attr['shape'] = shape
-        node.fluid_code.add_layer('reshape',
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            'reshape', inputs=val_x, output=node, param_attr=attr)
 
     def Cast(self, node):
         val_input = self.graph.get_input_node(node, idx=0, copy=True)
@@ -827,10 +799,8 @@ class ONNXOpMapper(OpMapper):
         if output_dtype:
             assert dtype == output_dtype, 'dtype of to unmatches output'
         attr = {'dtype': string(dtype)}
-        node.fluid_code.add_layer('cast',
-                                  inputs=val_input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            'cast', inputs=val_input, output=node, param_attr=attr)
 
     def AveragePool(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -865,10 +835,8 @@ class ONNXOpMapper(OpMapper):
             "name": string(node.layer_name)
         }
 
-        node.fluid_code.add_layer(fluid_op,
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            fluid_op, inputs=val_x, output=node, param_attr=attr)
 
     def Concat(self, node):
         inputs = []
@@ -880,19 +848,15 @@ class ONNXOpMapper(OpMapper):
                 inputs.append(ipt.layer_name)
         axis = node.get_attr('axis')
         attr = {'axis': axis}
-        node.fluid_code.add_layer('concat',
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            'concat', inputs=inputs, output=node, param_attr=attr)
 
     def Flatten(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         axis = node.get_attr('axis', 1)
         attr = {"axis": str(axis), "name": string(node.layer_name)}
-        node.fluid_code.add_layer('flatten',
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            'flatten', inputs=val_x, output=node, param_attr=attr)
 
     def Gemm(self, node):
         val_a = self.graph.get_input_node(node, idx=0, copy=True)
@@ -911,7 +875,8 @@ class ONNXOpMapper(OpMapper):
             "alpha": alpha,
             "name": string(val_mm)
         }
-        node.fluid_code.add_layer('matmul',
+        node.fluid_code.add_layer(
+            'matmul',
             inputs=matmul_inputs,
             output=val_mm,
             param_attr=attr_matmul)
@@ -920,21 +885,24 @@ class ONNXOpMapper(OpMapper):
             if beta == 1.:
                 add_inputs = {"x": val_mm, "y": val_c}
                 attr = {"name": string(node.layer_name)}
-                node.fluid_code.add_layer("elementwise_add",
+                node.fluid_code.add_layer(
+                    "elementwise_add",
                     inputs=add_inputs,
                     output=node,
                     param_attr=attr)
             else:
                 var_beta = node.layer_name + '_beta'
                 matmul_beta_inputs = {"x": val_c, "y": var_beta}
-                node.fluid_code.add_layer("Constant",
+                node.fluid_code.add_layer(
+                    "Constant",
                     inputs=matmul_beta_inputs,
                     output=var_beta,
                     param_attr={'value': beta})
 
                 add_inputs = {"x": val_mm, "y": var_beta}
                 attr = {"name": string(node.layer_name)}
-                node.fluid_code.add_layer("elementwise_add",
+                node.fluid_code.add_layer(
+                    "elementwise_add",
                     inputs=add_inputs,
                     output=node,
                     param_attr=attr)
@@ -942,8 +910,10 @@ class ONNXOpMapper(OpMapper):
     def Sum(self, node):
         val_inps = node.layer.input
         inputs = {
-            "x": self.graph.get_input_node(node, idx=0, copy=True),
-            "y": self.graph.get_input_node(node, idx=1, copy=True),
+            "x": self.graph.get_input_node(
+                node, idx=0, copy=True),
+            "y": self.graph.get_input_node(
+                node, idx=1, copy=True),
         }
         node.fluid_code.add_layer("elementwise_add", inputs=inputs, output=node)
 
@@ -953,19 +923,16 @@ class ONNXOpMapper(OpMapper):
                 "x": node.layer_name,
                 "y": y,
             }
-            node.fluid_code.add_layer("elementwise_add",
-                                      inputs=inputs,
-                                      output=node)
+            node.fluid_code.add_layer(
+                "elementwise_add", inputs=inputs, output=node)
 
     def MatMul(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_y = self.graph.get_input_node(node, idx=1, copy=True)
         inputs = {"x": val_x, "y": val_y}
         attr = {"name": string(node.layer_name)}
-        node.fluid_code.add_layer("matmul",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "matmul", inputs=inputs, output=node, param_attr=attr)
 
     def BatchNormalization(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -996,27 +963,21 @@ class ONNXOpMapper(OpMapper):
             "use_global_stats": spatial,
             "name": string(node.layer_name)
         }
-        node.fluid_code.add_layer("batch_norm",
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "batch_norm", inputs=val_x, output=node, param_attr=attr)
 
     def Transpose(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         perm = node.get_attr('perm')
         attr = {'perm': perm, "name": string(node.layer_name)}
-        node.fluid_code.add_layer("transpose",
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "transpose", inputs=val_x, output=node, param_attr=attr)
 
     def Relu(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         attr = {"name": string(node.layer_name)}
-        node.fluid_code.add_layer("relu",
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "relu", inputs=val_x, output=node, param_attr=attr)
 
     def PRelu(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -1032,28 +993,23 @@ class ONNXOpMapper(OpMapper):
             "param_attr": string(val_slope.layer_name),
             'mode': string(mode)
         }
-        node.fluid_code.add_layer("prelu",
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "prelu", inputs=val_x, output=node, param_attr=attr)
 
     def Squeeze(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         axes = node.get_attr('axes')
         attr = {'axes': axes, "name": string(node.layer_name)}
-        node.fluid_code.add_layer("squeeze",
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "squeeze", inputs=val_x, output=node, param_attr=attr)
 
     def Equal(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_y = self.graph.get_input_node(node, idx=1, copy=True)
-        node.fluid_code.add_layer("equal",
-                                  inputs={
-                                      'x': val_x,
-                                      'y': val_y
-                                  },
+        node.fluid_code.add_layer(
+            "equal",
+            inputs={'x': val_x,
+                    'y': val_y},
             output=node,
             param_attr=None)
 
@@ -1063,52 +1019,51 @@ class ONNXOpMapper(OpMapper):
         val_y = self.graph.get_input_node(node, idx=2, copy=True)
 
         not_condition = condition.layer_name + '_not'
-        node.fluid_code.add_layer("logical_not",
+        node.fluid_code.add_layer(
+            "logical_not",
             inputs=condition,
             output=not_condition,
             param_attr=None)
         cast_not_condition = not_condition + '_cast'
-        node.fluid_code.add_layer("cast",
+        node.fluid_code.add_layer(
+            "cast",
             inputs=not_condition,
             output=cast_not_condition,
             param_attr={'dtype': string(val_x.dtype)})
         cast_condition = condition.layer_name + '_cast'
-        node.fluid_code.add_layer("cast",
+        node.fluid_code.add_layer(
+            "cast",
             inputs=condition,
             output=cast_condition,
             param_attr={'dtype': string(val_x.dtype)})
         mul_val_x = val_x.layer_name + '_mul'
-        node.fluid_code.add_layer("elementwise_mul",
-                                  inputs={
-                                      'x': val_x,
-                                      'y': cast_condition
-                                  },
+        node.fluid_code.add_layer(
+            "elementwise_mul",
+            inputs={'x': val_x,
+                    'y': cast_condition},
             output=mul_val_x,
             param_attr=None)
 
         mul_val_y = val_y.layer_name + '_mul'
-        node.fluid_code.add_layer("elementwise_mul",
-                                  inputs={
-                                      'x': val_y,
-                                      'y': cast_not_condition
-                                  },
+        node.fluid_code.add_layer(
+            "elementwise_mul",
+            inputs={'x': val_y,
+                    'y': cast_not_condition},
             output=mul_val_y,
             param_attr=None)
 
-        node.fluid_code.add_layer("elementwise_add",
-                                  inputs={
-                                      'x': mul_val_x,
-                                      'y': mul_val_y
-                                  },
+        node.fluid_code.add_layer(
+            "elementwise_add",
+            inputs={'x': mul_val_x,
+                    'y': mul_val_y},
             output=node,
             param_attr=None)
 
     def NonZero(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         where_name = node.layer_name + '_where'
-        node.fluid_code.add_layer("where",
-                                  inputs=val_x.layer_name + '!=0',
-                                  output=where_name)
+        node.fluid_code.add_layer(
+            "where", inputs=val_x.layer_name + '!=0', output=where_name)
         dims = len(val_x.out_shapes[0])
         elements_count_val_x = reduce(lambda x, y: x * y, val_x.out_shapes[0])
         flatten_names = []
@@ -1121,17 +1076,15 @@ class ONNXOpMapper(OpMapper):
                 'starts': [0, dim],
                 'ends': [elements_count_val_x, dim + 1]
             }
-            node.fluid_code.add_layer("slice",
-                                      inputs=where_name,
-                                      output=slice_name,
-                                      param_attr=attr)
-            node.fluid_code.add_layer("flatten",
+            node.fluid_code.add_layer(
+                "slice", inputs=where_name, output=slice_name, param_attr=attr)
+            node.fluid_code.add_layer(
+                "flatten",
                 inputs=slice_name,
                 output=flatten_name,
                 param_attr={'axis': 0})
-        node.fluid_code.add_layer("concat",
-                                  inputs=flatten_names,
-                                  output=node,
+        node.fluid_code.add_layer(
+            "concat", inputs=flatten_names, output=node,
             param_attr={'axis': 0})
 
     def Identity(self, node):
@@ -1151,10 +1104,8 @@ class ONNXOpMapper(OpMapper):
             'expand_times': repeats,
             "name": string(node.layer_name),
         }
-        node.fluid_code.add_layer("expand",
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "expand", inputs=val_x, output=node, param_attr=attr)
 
     def MaxPool(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -1191,10 +1142,8 @@ class ONNXOpMapper(OpMapper):
             "name": string(node.layer_name),
             "exclusive": False
         }
-        node.fluid_code.add_layer(fluid_op,
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            fluid_op, inputs=val_x, output=node, param_attr=attr)
 
     def _global_pool(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -1220,10 +1169,8 @@ class ONNXOpMapper(OpMapper):
             "global_pooling": True,
             "name": string(node.layer_name)
         }
-        node.fluid_code.add_layer(fluid_op,
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            fluid_op, inputs=val_x, output=node, param_attr=attr)
 
     def GlobalMaxPool(self, node):
         self._global_pool(node)
@@ -1279,10 +1226,8 @@ class ONNXOpMapper(OpMapper):
             attr["bias_attr"] = string(val_b.layer_name)
         else:
             attr["bias_attr"] = False
-        node.fluid_code.add_layer(fluid_op,
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            fluid_op, inputs=val_x, output=node, param_attr=attr)
 
     def ConvTranspose(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -1314,11 +1259,11 @@ class ONNXOpMapper(OpMapper):
 
         output_size = [0, 0]
 
-        output_size[0] = (val_x.out_shapes[0][2] -
-                          1) * strides[0] - 2 * paddings[0] + dilations[0] * (
+        output_size[0] = (val_x.out_shapes[0][2] - 1
+                          ) * strides[0] - 2 * paddings[0] + dilations[0] * (
                               kernel_shape[0] - 1) + 1 + out_padding[0]
-        output_size[1] = (val_x.out_shapes[0][3] -
-                          1) * strides[1] - 2 * paddings[1] + dilations[1] * (
+        output_size[1] = (val_x.out_shapes[0][3] - 1
+                          ) * strides[1] - 2 * paddings[1] + dilations[1] * (
                               kernel_shape[1] - 1) + 1 + out_padding[1]
         attr = {
             'num_filters': num_out_channels,
@@ -1332,10 +1277,8 @@ class ONNXOpMapper(OpMapper):
             'bias_attr': None if val_b is None else string(val_b.layer_name),
             'name': string(node.layer_name),
         }
-        node.fluid_code.add_layer(fluid_op,
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            fluid_op, inputs=val_x, output=node, param_attr=attr)
 
     def GRU(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -1352,15 +1295,13 @@ class ONNXOpMapper(OpMapper):
         else:
             miss_arg_num += 1
         if num_ipt > 4 and node.layer.input[4] != '':
-            val_len = self.graph.get_input_node(node,
-                                                idx=4 - miss_arg_num,
-                                                copy=True)
+            val_len = self.graph.get_input_node(
+                node, idx=4 - miss_arg_num, copy=True)
         else:
             miss_arg_num += 1
         if num_ipt > 5 and node.layer.input[5] != '':
-            val_xh = self.graph.get_input_node(node,
-                                               idx=5 - miss_arg_num,
-                                               copy=True)
+            val_xh = self.graph.get_input_node(
+                node, idx=5 - miss_arg_num, copy=True)
 
         data, dtype, shape = self.get_dynamic_shape(val_x.layer_name)
 
@@ -1401,30 +1342,27 @@ class ONNXOpMapper(OpMapper):
         is_reverse = direction == 'reverse'
 
         var_x0 = node.layer_name + '_x0'
-        node.fluid_code.add_layer('squeeze',
+        node.fluid_code.add_layer(
+            'squeeze',
             inputs=val_x,
             output=var_x0,
-                                  param_attr={
-                                      'axes': [1],
-                                      'name': string(var_x0)
-                                  })
+            param_attr={'axes': [1],
+                        'name': string(var_x0)})
 
         var_w0 = node.layer_name + '_w0'
-        node.fluid_code.add_layer('squeeze',
+        node.fluid_code.add_layer(
+            'squeeze',
             inputs=val_w,
             output=var_w0,
-                                  param_attr={
-                                      'axes': [0],
-                                      'name': string(var_w0)
-                                  })
+            param_attr={'axes': [0],
+                        'name': string(var_w0)})
 
         var_fc = node.layer_name + '_fc'
         var_mm = (node.layer_name + '_mm') if val_b else var_fc
-        node.fluid_code.add_layer('matmul',
-                                  inputs={
-                                      'x': var_x0,
-                                      'y': var_w0
-                                  },
+        node.fluid_code.add_layer(
+            'matmul',
+            inputs={'x': var_x0,
+                    'y': var_w0},
             output=var_mm,
             param_attr={
                 'transpose_x': 0,
@@ -1433,65 +1371,58 @@ class ONNXOpMapper(OpMapper):
             })
 
         var_r0 = node.layer_name + '_r0'
-        node.fluid_code.add_layer('squeeze',
+        node.fluid_code.add_layer(
+            'squeeze',
             inputs=val_r,
             output=var_r0,
-                                  param_attr={
-                                      'axes': [0],
-                                      'name': string(var_r0)
-                                  })
+            param_attr={'axes': [0],
+                        'name': string(var_r0)})
 
         var_r0t = node.layer_name + '_r0t'
 
-        node.fluid_code.add_layer('transpose',
+        node.fluid_code.add_layer(
+            'transpose',
             inputs=var_r0,
             output=var_r0t,
-                                  param_attr={
-                                      'perm': [1, 0],
-                                      'name': string(var_r0t)
-                                  })
+            param_attr={'perm': [1, 0],
+                        'name': string(var_r0t)})
         if val_b:
             var_bi = node.layer_name + '_bi'
             var_bh = node.layer_name + '_bh'
-            node.fluid_code.add_layer('split',
+            node.fluid_code.add_layer(
+                'split',
                 inputs=val_b,
                 output=var_bi + ',' + var_bh,
                 param_attr={
-                                          'axis':
-                                          1,
-                                          'split':
-                                          [hidden_size * 3, hidden_size * 3],
-                                          'name':
-                                          string(node.layer_name + '.b/split')
+                    'axis': 1,
+                    'split': [hidden_size * 3, hidden_size * 3],
+                    'name': string(node.layer_name + '.b/split')
                 })
             var_bi0 = node.layer_name + '_bi0'
-            node.fluid_code.add_layer('squeeze',
+            node.fluid_code.add_layer(
+                'squeeze',
                 inputs=var_bi,
                 output=var_bi0,
-                                      param_attr={
-                                          'axes': [0],
-                                          'name': string(var_bi0)
-                                      })
+                param_attr={'axes': [0],
+                            'name': string(var_bi0)})
 
-            node.fluid_code.add_layer('elmentwise_add',
+            node.fluid_code.add_layer(
+                'elmentwise_add',
                 inputs=[var_mm, var_bi0],
                 output=var_fc,
                 param_attr={
-                                          'axes':
-                                          1,
-                                          'name':
-                                          string(node.layer_name + '.i/bias')
+                    'axes': 1,
+                    'name': string(node.layer_name + '.i/bias')
                 })
 
         if val_xh:
             var_xh0 = node.layer_name + '_xh0'
-            node.fluid_code.add_layer('squeeze',
+            node.fluid_code.add_layer(
+                'squeeze',
                 inputs=val_xh,
                 output=var_xh0,
-                                      param_attr={
-                                          'axes': [1],
-                                          'name': string(var_xh0)
-                                      })
+                param_attr={'axes': [1],
+                            'name': string(var_xh0)})
         var_y00 = node.layer_name + '_y00'
 
         attr = {
@@ -1503,7 +1434,8 @@ class ONNXOpMapper(OpMapper):
             'param_attr': string(var_r0t),
             'bias_attr': string(var_bh) if val_b else False,
         }
-        node.fluid_code.add_layer('dynamic_gru',
+        node.fluid_code.add_layer(
+            'dynamic_gru',
             inputs=var_fc + ',' + str(hidden_size),
             output=var_y00,
             param_attr=attr)
@@ -1511,7 +1443,8 @@ class ONNXOpMapper(OpMapper):
         num_opt = len(node.layer.output)
 
         if num_opt > 0 and node.layer.output[0] != '':
-            node.fluid_code.add_layer('unsqueeze',
+            node.fluid_code.add_layer(
+                'unsqueeze',
                 inputs=var_y00,
                 output=node.layer.output[0],
                 param_attr={
@@ -1519,7 +1452,8 @@ class ONNXOpMapper(OpMapper):
                     'name': string(node.layer.output[0])
                 })
         if num_opt > 1 and node.layer.output[1] != '':
-            node.fluid_code.add_layer('unsqueeze',
+            node.fluid_code.add_layer(
+                'unsqueeze',
                 inputs=var_y00,
                 output=node.layer.output[1],
                 param_attr={

x2paddle/op_mapper/paddle_custom_layer/im2sequence.py

+import onnx
+import numpy as np
+from onnx import onnx_pb, helper
+
+im2seq_counter = 0
+
+
+def im2sequence(op, block):
+    global im2sequence_counter
+    n, c, h, w = block.var(op.input('X')[0]).shape
+    assert h > 0 and w > 0, "Only supported fixed input shape for im2sequence operator."
+    stride_h, stride_w = op.attr('strides')
+    paddings = op.attr('paddings')
+    assert op.attr(
+        'out_stride'
+    ) != 1, "Only out_stride==1 is supported for im2sequence operator."
+    h = h + paddings[0] + paddings[1]
+    w = w + paddings[1] + paddings[2]
+    kernel_h, kernel_w = op.attr('kernels')
+    out_h = 1 + (h - kernel_h + stride_h - 1) // stride_h
+    out_w = 1 + (w - kernel_w + stride_w - 1) // stride_w
+    h_steps = list()
+    for i in range(out_h):
+        h_steps.append([i * stride_h, i * stride_h + kernel_h])
+    w_steps = list()
+    for i in range(out_w):
+        w_steps.append([i * stride_w, i * stride_w + kernel_w])
+
+    nodes = list()
+    slice_blocks = list()
+    for i in range(out_h):
+        for j in range(out_w):
+            starts_name = "im2sequence.starts.{}.{}.{}".format(im2seq_counter,
+                                                               i, j)
+            starts_tensor = helper.make_tensor(
+                name=starts_name,
+                data_type=onnx_pb.TensorProto.INT64,
+                dims=[4],
+                vals=[0, 0, h_steps[i][0], w_steps[j][0]])
+            ends_name = "im2sequence.ends.{}.{}.{}".format(im2seq_counter, i, j)
+            ends_tensor = helper.make_tensor(
+                name=ends_name,
+                data_type=onnx_pb.TensorProto.INT64,
+                dims=[4],
+                vals=[999999, 999999, h_steps[i][1], w_steps[j][1]])
+            starts_node = helper.make_node(
+                'Constant',
+                inputs=[],
+                outputs=[starts_name],
+                value=starts_tensor)
+            ends_node = helper.make_node(
+                'Constant', inputs=[], outputs=[ends_name], value=ends_tensor)
+            nodes.extend([starts_node, ends_node])
+
+            slice_block_name = "im2sequence.slice.{}.{}.{}".format(
+                im2seq_counter, i, j)
+            slice_block_node = helper.make_node(
+                'Slice',
+                inputs=[op.input('X')[0], starts_name, ends_name],
+                outputs=[slice_block_name])
+            flatten_block_name = "im2sequence.flatten.{}.{}.{}".format(
+                im2seq_counter, i, j)
+            flatten_block_node = helper.make_node(
+                "Flatten",
+                inputs=[slice_block_name],
+                outputs=[flatten_block_name],
+                axis=0)
+            nodes.extend([slice_block_node, flatten_block_node])
+            slice_blocks.append(flatten_block_name)
+    concat_block_name = "im2sequence.concat_block.{}".format(im2seq_counter)
+    #    concat_block_node = helper.make_node("Concat", inputs=slice_blocks, outputs=[concat_block_name], axis=0)
+    concat_block_node = helper.make_node(
+        "Concat", inputs=slice_blocks, outputs=op.output('Out'), axis=0)
+    nodes.append(concat_block_node)
+    print("\n\n==========Importance Notice===========")
+    print(
+        "Since im2sequence operator is used in your paddlepaddle model, the translated onnx model only support input data with batch_size=1."
+    )
+    print("======================================\n")
+    return nodes

x2paddle/op_mapper/paddle_custom_layer/multiclass_nms.py

+#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import sys
+import os
+import numpy as np
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+import onnx
+import warnings
+from onnx import helper, onnx_pb
+
+
+def multiclass_nms(op, block):
+    """
+    Convert the paddle multiclass_nms to onnx op.
+    This op is get the select boxes from origin boxes.
+    """
+    inputs = dict()
+    outputs = dict()
+    attrs = dict()
+    for name in op.input_names:
+        inputs[name] = op.input(name)
+    for name in op.output_names:
+        outputs[name] = op.output(name)
+    for name in op.attr_names:
+        attrs[name] = op.attr(name)
+
+    result_name = outputs['Out'][0]
+    background = attrs['background_label']
+    normalized = attrs['normalized']
+    if normalized == False:
+        warnings.warn(
+            'The parameter normalized of multiclass_nms OP of Paddle is False, which has diff with ONNX. \
+                         Please set normalized=True in multiclass_nms of Paddle')
+
+    #convert the paddle attribute to onnx tensor
+    name_score_threshold = [outputs['Out'][0] + "@score_threshold"]
+    name_iou_threshold = [outputs['Out'][0] + "@iou_threshold"]
+    name_keep_top_k = [outputs['Out'][0] + '@keep_top_k']
+    name_keep_top_k_2D = [outputs['Out'][0] + '@keep_top_k_1D']
+
+    node_score_threshold = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_score_threshold,
+        value=onnx.helper.make_tensor(
+            name=name_score_threshold[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=(),
+            vals=[float(attrs['score_threshold'])]))
+
+    node_iou_threshold = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_iou_threshold,
+        value=onnx.helper.make_tensor(
+            name=name_iou_threshold[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=(),
+            vals=[float(attrs['nms_threshold'])]))
+
+    node_keep_top_k = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_keep_top_k,
+        value=onnx.helper.make_tensor(
+            name=name_keep_top_k[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=(),
+            vals=[np.int64(attrs['keep_top_k'])]))
+
+    node_keep_top_k_2D = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_keep_top_k_2D,
+        value=onnx.helper.make_tensor(
+            name=name_keep_top_k_2D[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=[1, 1],
+            vals=[np.int64(attrs['keep_top_k'])]))
+
+    # the paddle data format is x1,y1,x2,y2
+    kwargs = {'center_point_box': 0}
+
+    name_select_nms = [outputs['Out'][0] + "@select_index"]
+    node_select_nms= onnx.helper.make_node(
+        'NonMaxSuppression',
+        inputs=inputs['BBoxes'] + inputs['Scores'] + name_keep_top_k +\
+            name_iou_threshold + name_score_threshold,
+        outputs=name_select_nms)
+    # step 1 nodes select the nms class
+    node_list = [
+        node_score_threshold, node_iou_threshold, node_keep_top_k,
+        node_keep_top_k_2D, node_select_nms
+    ]
+
+    # create some const value to use
+    name_const_value = [result_name+"@const_0",
+        result_name+"@const_1",\
+        result_name+"@const_2",\
+        result_name+"@const_-1"]
+    value_const_value = [0, 1, 2, -1]
+    for name, value in zip(name_const_value, value_const_value):
+        node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=[name],
+            value=onnx.helper.make_tensor(
+                name=name + "@const",
+                data_type=onnx.TensorProto.INT64,
+                dims=[1],
+                vals=[value]))
+        node_list.append(node)
+
+    # Ine this code block, we will deocde the raw score data, reshape N * C * M to 1 * N*C*M
+    # and the same time, decode the select indices to 1 * D, gather the select_indices
+    outputs_gather_1 = [result_name + "@gather_1"]
+    node_gather_1 = onnx.helper.make_node(
+        'Gather',
+        inputs=name_select_nms + [result_name + "@const_1"],
+        outputs=outputs_gather_1,
+        axis=1)
+    node_list.append(node_gather_1)
+
+    outputs_squeeze_gather_1 = [result_name + "@sequeeze_gather_1"]
+    node_squeeze_gather_1 = onnx.helper.make_node(
+        'Squeeze',
+        inputs=outputs_gather_1,
+        outputs=outputs_squeeze_gather_1,
+        axes=[1])
+    node_list.append(node_squeeze_gather_1)
+
+    outputs_gather_2 = [result_name + "@gather_2"]
+    node_gather_2 = onnx.helper.make_node(
+        'Gather',
+        inputs=name_select_nms + [result_name + "@const_2"],
+        outputs=outputs_gather_2,
+        axis=1)
+    node_list.append(node_gather_2)
+
+    #slice the class is not 0
+    if background == 0:
+        outputs_nonzero = [result_name + "@nonzero"]
+        node_nonzero = onnx.helper.make_node(
+            'NonZero', inputs=outputs_squeeze_gather_1, outputs=outputs_nonzero)
+        node_list.append(node_nonzero)
+    else:
+        name_thresh = [result_name + "@thresh"]
+        node_thresh = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=name_thresh,
+            value=onnx.helper.make_tensor(
+                name=name_thresh[0] + "@const",
+                data_type=onnx.TensorProto.INT32,
+                dims=[1],
+                vals=[-1]))
+        node_list.append(node_thresh)
+
+        outputs_cast = [result_name + "@cast"]
+        node_cast = onnx.helper.make_node(
+            'Cast', inputs=outputs_squeeze_gather_1, outputs=outputs_cast, to=6)
+        node_list.append(node_cast)
+
+        outputs_greater = [result_name + "@greater"]
+        node_greater = onnx.helper.make_node(
+            'Greater',
+            inputs=outputs_cast + name_thresh,
+            outputs=outputs_greater)
+        node_list.append(node_greater)
+
+        outputs_nonzero = [result_name + "@nonzero"]
+        node_nonzero = onnx.helper.make_node(
+            'NonZero', inputs=outputs_greater, outputs=outputs_nonzero)
+        node_list.append(node_nonzero)
+
+    outputs_gather_1_nonzero = [result_name + "@gather_1_nonzero"]
+    node_gather_1_nonzero = onnx.helper.make_node(
+        'Gather',
+        inputs=outputs_gather_1 + outputs_nonzero,
+        outputs=outputs_gather_1_nonzero,
+        axis=0)
+    node_list.append(node_gather_1_nonzero)
+
+    outputs_gather_2_nonzero = [result_name + "@gather_2_nonzero"]
+    node_gather_2_nonzero = onnx.helper.make_node(
+        'Gather',
+        inputs=outputs_gather_2 + outputs_nonzero,
+        outputs=outputs_gather_2_nonzero,
+        axis=0)
+    node_list.append(node_gather_2_nonzero)
+
+    # reshape scores N * C * M to (N*C*M) * 1
+    outputs_reshape_scores_rank1 = [result_name + "@reshape_scores_rank1"]
+    node_reshape_scores_rank1 = onnx.helper.make_node(
+        "Reshape",
+        inputs=inputs['Scores'] + [result_name + "@const_-1"],
+        outputs=outputs_reshape_scores_rank1)
+    node_list.append(node_reshape_scores_rank1)
+
+    # get the shape of scores
+    outputs_shape_scores = [result_name + "@shape_scores"]
+    node_shape_scores = onnx.helper.make_node(
+        'Shape', inputs=inputs['Scores'], outputs=outputs_shape_scores)
+    node_list.append(node_shape_scores)
+
+    # gather the index: 2 shape of scores
+    outputs_gather_scores_dim1 = [result_name + "@gather_scores_dim1"]
+    node_gather_scores_dim1 = onnx.helper.make_node(
+        'Gather',
+        inputs=outputs_shape_scores + [result_name + "@const_2"],
+        outputs=outputs_gather_scores_dim1,
+        axis=0)
+    node_list.append(node_gather_scores_dim1)
+
+    # mul class * M
+    outputs_mul_classnum_boxnum = [result_name + "@mul_classnum_boxnum"]
+    node_mul_classnum_boxnum = onnx.helper.make_node(
+        'Mul',
+        inputs=outputs_gather_1_nonzero + outputs_gather_scores_dim1,
+        outputs=outputs_mul_classnum_boxnum)
+    node_list.append(node_mul_classnum_boxnum)
+
+    # add class * M * index
+    outputs_add_class_M_index = [result_name + "@add_class_M_index"]
+    node_add_class_M_index = onnx.helper.make_node(
+        'Add',
+        inputs=outputs_mul_classnum_boxnum + outputs_gather_2_nonzero,
+        outputs=outputs_add_class_M_index)
+    node_list.append(node_add_class_M_index)
+
+    # Squeeze the indices to 1 dim
+    outputs_squeeze_select_index = [result_name + "@squeeze_select_index"]
+    node_squeeze_select_index = onnx.helper.make_node(
+        'Squeeze',
+        inputs=outputs_add_class_M_index,
+        outputs=outputs_squeeze_select_index,
+        axes=[0, 2])
+    node_list.append(node_squeeze_select_index)
+
+    # gather the data from flatten scores
+    outputs_gather_select_scores = [result_name + "@gather_select_scores"]
+    node_gather_select_scores = onnx.helper.make_node('Gather',
+        inputs=outputs_reshape_scores_rank1 + \
+            outputs_squeeze_select_index,
+        outputs=outputs_gather_select_scores,
+        axis=0)
+    node_list.append(node_gather_select_scores)
+
+    # get nums to input TopK
+    outputs_shape_select_num = [result_name + "@shape_select_num"]
+    node_shape_select_num = onnx.helper.make_node(
+        'Shape',
+        inputs=outputs_gather_select_scores,
+        outputs=outputs_shape_select_num)
+    node_list.append(node_shape_select_num)
+
+    outputs_gather_select_num = [result_name + "@gather_select_num"]
+    node_gather_select_num = onnx.helper.make_node(
+        'Gather',
+        inputs=outputs_shape_select_num + [result_name + "@const_0"],
+        outputs=outputs_gather_select_num,
+        axis=0)
+    node_list.append(node_gather_select_num)
+
+    outputs_unsqueeze_select_num = [result_name + "@unsqueeze_select_num"]
+    node_unsqueeze_select_num = onnx.helper.make_node(
+        'Unsqueeze',
+        inputs=outputs_gather_select_num,
+        outputs=outputs_unsqueeze_select_num,
+        axes=[0])
+    node_list.append(node_unsqueeze_select_num)
+
+    outputs_concat_topK_select_num = [result_name + "@conat_topK_select_num"]
+    node_conat_topK_select_num = onnx.helper.make_node(
+        'Concat',
+        inputs=outputs_unsqueeze_select_num + name_keep_top_k_2D,
+        outputs=outputs_concat_topK_select_num,
+        axis=0)
+    node_list.append(node_conat_topK_select_num)
+
+    outputs_cast_concat_topK_select_num = [
+        result_name + "@concat_topK_select_num"
+    ]
+    node_outputs_cast_concat_topK_select_num = onnx.helper.make_node(
+        'Cast',
+        inputs=outputs_concat_topK_select_num,
+        outputs=outputs_cast_concat_topK_select_num,
+        to=6)
+    node_list.append(node_outputs_cast_concat_topK_select_num)
+    # get min(topK, num_select)
+    outputs_compare_topk_num_select = [result_name + "@compare_topk_num_select"]
+    node_compare_topk_num_select = onnx.helper.make_node(
+        'ReduceMin',
+        inputs=outputs_cast_concat_topK_select_num,
+        outputs=outputs_compare_topk_num_select,
+        keepdims=0)
+    node_list.append(node_compare_topk_num_select)
+
+    # unsqueeze the indices to 1D tensor
+    outputs_unsqueeze_topk_select_indices = [
+        result_name + "@unsqueeze_topk_select_indices"
+    ]
+    node_unsqueeze_topk_select_indices = onnx.helper.make_node(
+        'Unsqueeze',
+        inputs=outputs_compare_topk_num_select,
+        outputs=outputs_unsqueeze_topk_select_indices,
+        axes=[0])
+    node_list.append(node_unsqueeze_topk_select_indices)
+
+    # cast the indices to INT64
+    outputs_cast_topk_indices = [result_name + "@cast_topk_indices"]
+    node_cast_topk_indices = onnx.helper.make_node(
+        'Cast',
+        inputs=outputs_unsqueeze_topk_select_indices,
+        outputs=outputs_cast_topk_indices,
+        to=7)
+    node_list.append(node_cast_topk_indices)
+
+    # select topk scores  indices
+    outputs_topk_select_topk_indices = [result_name + "@topk_select_topk_values",\
+        result_name + "@topk_select_topk_indices"]
+    node_topk_select_topk_indices = onnx.helper.make_node(
+        'TopK',
+        inputs=outputs_gather_select_scores + outputs_cast_topk_indices,
+        outputs=outputs_topk_select_topk_indices)
+    node_list.append(node_topk_select_topk_indices)
+
+    # gather topk label, scores, boxes
+    outputs_gather_topk_scores = [result_name + "@gather_topk_scores"]
+    node_gather_topk_scores = onnx.helper.make_node(
+        'Gather',
+        inputs=outputs_gather_select_scores +
+        [outputs_topk_select_topk_indices[1]],
+        outputs=outputs_gather_topk_scores,
+        axis=0)
+    node_list.append(node_gather_topk_scores)
+
+    outputs_gather_topk_class = [result_name + "@gather_topk_class"]
+    node_gather_topk_class = onnx.helper.make_node(
+        'Gather',
+        inputs=outputs_gather_1_nonzero +
+        [outputs_topk_select_topk_indices[1]],
+        outputs=outputs_gather_topk_class,
+        axis=1)
+    node_list.append(node_gather_topk_class)
+
+    # gather the boxes need to gather the boxes id, then get boxes
+    outputs_gather_topk_boxes_id = [result_name + "@gather_topk_boxes_id"]
+    node_gather_topk_boxes_id = onnx.helper.make_node(
+        'Gather',
+        inputs=outputs_gather_2_nonzero +
+        [outputs_topk_select_topk_indices[1]],
+        outputs=outputs_gather_topk_boxes_id,
+        axis=1)
+    node_list.append(node_gather_topk_boxes_id)
+
+    # squeeze the gather_topk_boxes_id to 1 dim
+    outputs_squeeze_topk_boxes_id = [result_name + "@squeeze_topk_boxes_id"]
+    node_squeeze_topk_boxes_id = onnx.helper.make_node(
+        'Squeeze',
+        inputs=outputs_gather_topk_boxes_id,
+        outputs=outputs_squeeze_topk_boxes_id,
+        axes=[0, 2])
+    node_list.append(node_squeeze_topk_boxes_id)
+
+    outputs_gather_select_boxes = [result_name + "@gather_select_boxes"]
+    node_gather_select_boxes = onnx.helper.make_node(
+        'Gather',
+        inputs=inputs['BBoxes'] + outputs_squeeze_topk_boxes_id,
+        outputs=outputs_gather_select_boxes,
+        axis=1)
+    node_list.append(node_gather_select_boxes)
+
+    # concat the final result
+    # before concat need to cast the class to float
+    outputs_cast_topk_class = [result_name + "@cast_topk_class"]
+    node_cast_topk_class = onnx.helper.make_node(
+        'Cast',
+        inputs=outputs_gather_topk_class,
+        outputs=outputs_cast_topk_class,
+        to=1)
+    node_list.append(node_cast_topk_class)
+
+    outputs_unsqueeze_topk_scores = [result_name + "@unsqueeze_topk_scores"]
+    node_unsqueeze_topk_scores = onnx.helper.make_node(
+        'Unsqueeze',
+        inputs=outputs_gather_topk_scores,
+        outputs=outputs_unsqueeze_topk_scores,
+        axes=[0, 2])
+    node_list.append(node_unsqueeze_topk_scores)
+
+    inputs_concat_final_results = outputs_cast_topk_class + outputs_unsqueeze_topk_scores +\
+        outputs_gather_select_boxes
+    outputs_concat_final_results = outputs['Out']
+    node_concat_final_results = onnx.helper.make_node(
+        'Concat',
+        inputs=inputs_concat_final_results,
+        outputs=outputs_concat_final_results,
+        axis=2)
+    node_list.append(node_concat_final_results)
+
+    return node_list

x2paddle/op_mapper/paddle_custom_layer/yolo_box.py

+import onnx
+import numpy as np
+from onnx import onnx_pb, helper
+
+
+def get_old_name(arg, name_prefix=''):
+    prefix_index = arg.find(name_prefix)
+
+    if prefix_index != -1:
+        last_prefix = arg[len(name_prefix):]
+    else:
+        last_prefix = arg
+    idx = last_prefix.find('@')
+    if idx != -1:
+        last_prefix = last_prefix[:idx]
+    return name_prefix + last_prefix
+
+
+def yolo_box(op, block):
+    inputs = dict()
+    outputs = dict()
+    attrs = dict()
+    for name in op.input_names:
+        inputs[name] = op.input(name)
+    for name in op.output_names:
+        outputs[name] = op.output(name)
+    for name in op.attr_names:
+        attrs[name] = op.attr(name)
+    model_name = outputs['Boxes'][0]
+    input_shape = block.vars[get_old_name(inputs['X'][0])].shape
+    image_size = inputs['ImgSize']
+    input_height = input_shape[2]
+    input_width = input_shape[3]
+
+    class_num = attrs['class_num']
+    anchors = attrs['anchors']
+    num_anchors = int(len(anchors)) // 2
+    downsample_ratio = attrs['downsample_ratio']
+    input_size = input_height * downsample_ratio
+    conf_thresh = attrs['conf_thresh']
+    conf_thresh_mat = np.ones([num_anchors * input_height *
+                               input_width]) * conf_thresh
+
+    node_list = []
+    im_outputs = []
+
+    x_shape = [1, num_anchors, 5 + class_num, input_height, input_width]
+    name_x_shape = [model_name + "@x_shape"]
+    node_x_shape = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_x_shape,
+        value=onnx.helper.make_tensor(
+            name=name_x_shape[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=[5],
+            vals=x_shape))
+    node_list.append(node_x_shape)
+
+    outputs_x_reshape = [model_name + "@reshape"]
+    node_x_reshape = onnx.helper.make_node(
+        'Reshape', inputs=inputs['X'] + name_x_shape, outputs=outputs_x_reshape)
+    node_list.append(node_x_reshape)
+
+    outputs_x_transpose = [model_name + "@x_transpose"]
+    node_x_transpose = onnx.helper.make_node(
+        'Transpose',
+        inputs=outputs_x_reshape,
+        outputs=outputs_x_transpose,
+        perm=[0, 1, 3, 4, 2])
+    node_list.append(node_x_transpose)
+
+    range_x = []
+    range_y = []
+    for i in range(0, input_width):
+        range_x.append(i)
+    for j in range(0, input_height):
+        range_y.append(j)
+
+    name_range_x = [model_name + "@range_x"]
+    node_range_x = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_range_x,
+        value=onnx.helper.make_tensor(
+            name=name_range_x[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=[input_width],
+            vals=range_x))
+    node_list.append(node_range_x)
+
+    name_range_y = [model_name + "@range_y"]
+    node_range_y = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_range_y,
+        value=onnx.helper.make_tensor(
+            name=name_range_y[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=[input_height],
+            vals=range_y))
+    node_list.append(node_range_y)
+
+    range_x_new_shape = [1, input_width]
+    range_y_new_shape = [input_height, 1]
+
+    name_range_x_new_shape = [model_name + "@range_x_new_shape"]
+    node_range_x_new_shape = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_range_x_new_shape,
+        value=onnx.helper.make_tensor(
+            name=name_range_x_new_shape[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=[len(range_x_new_shape)],
+            vals=range_x_new_shape))
+    node_list.append(node_range_x_new_shape)
+
+    name_range_y_new_shape = [model_name + "@range_y_new_shape"]
+    node_range_y_new_shape = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_range_y_new_shape,
+        value=onnx.helper.make_tensor(
+            name=name_range_y_new_shape[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=[len(range_y_new_shape)],
+            vals=range_y_new_shape))
+    node_list.append(node_range_y_new_shape)
+
+    outputs_range_x_reshape = [model_name + "@range_x_reshape"]
+    node_range_x_reshape = onnx.helper.make_node(
+        'Reshape',
+        inputs=name_range_x + name_range_x_new_shape,
+        outputs=outputs_range_x_reshape)
+    node_list.append(node_range_x_reshape)
+
+    outputs_range_y_reshape = [model_name + "@range_y_reshape"]
+    node_range_y_reshape = onnx.helper.make_node(
+        'Reshape',
+        inputs=name_range_y + name_range_y_new_shape,
+        outputs=outputs_range_y_reshape)
+    node_list.append(node_range_y_reshape)
+
+    outputs_grid_x = [model_name + "@grid_x"]
+    node_grid_x = onnx.helper.make_node(
+        "Tile",
+        inputs=outputs_range_x_reshape + name_range_y_new_shape,
+        outputs=outputs_grid_x)
+    node_list.append(node_grid_x)
+
+    outputs_grid_y = [model_name + "@grid_y"]
+    node_grid_y = onnx.helper.make_node(
+        "Tile",
+        inputs=outputs_range_y_reshape + name_range_x_new_shape,
+        outputs=outputs_grid_y)
+    node_list.append(node_grid_y)
+
+    outputs_box_x = [model_name + "@box_x"]
+    outputs_box_y = [model_name + "@box_y"]
+    outputs_box_w = [model_name + "@box_w"]
+    outputs_box_h = [model_name + "@box_h"]
+    outputs_conf = [model_name + "@conf"]
+    outputs_prob = [model_name + "@prob"]
+
+    node_split_input = onnx.helper.make_node(
+        "Split",
+        inputs=outputs_x_transpose,
+        outputs=outputs_box_x + outputs_box_y + outputs_box_w\
+                + outputs_box_h + outputs_conf + outputs_prob,
+        axis=-1,
+        split=[1, 1, 1, 1, 1, class_num])
+    node_list.append(node_split_input)
+
+    outputs_box_x_sigmoid = [model_name + "@box_x_sigmoid"]
+    outputs_box_y_sigmoid = [model_name + "@box_y_sigmoid"]
+
+    node_box_x_sigmoid = onnx.helper.make_node(
+        "Sigmoid", inputs=outputs_box_x, outputs=outputs_box_x_sigmoid)
+    node_list.append(node_box_x_sigmoid)
+
+    node_box_y_sigmoid = onnx.helper.make_node(
+        "Sigmoid", inputs=outputs_box_y, outputs=outputs_box_y_sigmoid)
+    node_list.append(node_box_y_sigmoid)
+
+    outputs_box_x_squeeze = [model_name + "@box_x_squeeze"]
+    outputs_box_y_squeeze = [model_name + "@box_y_squeeze"]
+
+    node_box_x_squeeze = onnx.helper.make_node(
+        'Squeeze',
+        inputs=outputs_box_x_sigmoid,
+        outputs=outputs_box_x_squeeze,
+        axes=[4])
+    node_list.append(node_box_x_squeeze)
+
+    node_box_y_squeeze = onnx.helper.make_node(
+        'Squeeze',
+        inputs=outputs_box_y_sigmoid,
+        outputs=outputs_box_y_squeeze,
+        axes=[4])
+    node_list.append(node_box_y_squeeze)
+
+    outputs_box_x_add_grid = [model_name + "@box_x_add_grid"]
+    outputs_box_y_add_grid = [model_name + "@box_y_add_grid"]
+
+    node_box_x_add_grid = onnx.helper.make_node(
+        "Add",
+        inputs=outputs_grid_x + outputs_box_x_squeeze,
+        outputs=outputs_box_x_add_grid)
+    node_list.append(node_box_x_add_grid)
+
+    node_box_y_add_grid = onnx.helper.make_node(
+        "Add",
+        inputs=outputs_grid_y + outputs_box_y_squeeze,
+        outputs=outputs_box_y_add_grid)
+    node_list.append(node_box_y_add_grid)
+
+    name_input_h = [model_name + "@input_h"]
+    name_input_w = [model_name + "@input_w"]
+
+    node_input_h = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_input_h,
+        value=onnx.helper.make_tensor(
+            name=name_input_w[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=(),
+            vals=[input_height]))
+    node_list.append(node_input_h)
+
+    node_input_w = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_input_w,
+        value=onnx.helper.make_tensor(
+            name=name_input_w[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=(),
+            vals=[input_width]))
+    node_list.append(node_input_w)
+
+    outputs_box_x_encode = [model_name + "@box_x_encode"]
+    outputs_box_y_encode = [model_name + "@box_y_encode"]
+
+    node_box_x_encode = onnx.helper.make_node(
+        'Div',
+        inputs=outputs_box_x_add_grid + name_input_w,
+        outputs=outputs_box_x_encode)
+    node_list.append(node_box_x_encode)
+
+    node_box_y_encode = onnx.helper.make_node(
+        'Div',
+        inputs=outputs_box_y_add_grid + name_input_h,
+        outputs=outputs_box_y_encode)
+    node_list.append(node_box_y_encode)
+
+    name_anchor_tensor = [model_name + "@anchor_tensor"]
+    node_anchor_tensor = onnx.helper.make_node(
+        "Constant",
+        inputs=[],
+        outputs=name_anchor_tensor,
+        value=onnx.helper.make_tensor(
+            name=name_anchor_tensor[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=[len(anchors)],
+            vals=anchors))
+    node_list.append(node_anchor_tensor)
+
+    anchor_shape = [int(num_anchors), 2]
+    name_anchor_shape = [model_name + "@anchor_shape"]
+    node_anchor_shape = onnx.helper.make_node(
+        "Constant",
+        inputs=[],
+        outputs=name_anchor_shape,
+        value=onnx.helper.make_tensor(
+            name=name_anchor_shape[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=[2],
+            vals=anchor_shape))
+    node_list.append(node_anchor_shape)
+
+    outputs_anchor_tensor_reshape = [model_name + "@anchor_tensor_reshape"]
+    node_anchor_tensor_reshape = onnx.helper.make_node(
+        "Reshape",
+        inputs=name_anchor_tensor + name_anchor_shape,
+        outputs=outputs_anchor_tensor_reshape)
+    node_list.append(node_anchor_tensor_reshape)
+
+    name_input_size = [model_name + "@input_size"]
+    node_input_size = onnx.helper.make_node(
+        "Constant",
+        inputs=[],
+        outputs=name_input_size,
+        value=onnx.helper.make_tensor(
+            name=name_input_size[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=(),
+            vals=[input_size]))
+    node_list.append(node_input_size)
+
+    outputs_anchors_div_input_size = [model_name + "@anchors_div_input_size"]
+    node_anchors_div_input_size = onnx.helper.make_node(
+        "Div",
+        inputs=outputs_anchor_tensor_reshape + name_input_size,
+        outputs=outputs_anchors_div_input_size)
+    node_list.append(node_anchors_div_input_size)
+
+    outputs_anchor_w = [model_name + "@anchor_w"]
+    outputs_anchor_h = [model_name + "@anchor_h"]
+
+    node_anchor_split = onnx.helper.make_node(
+        'Split',
+        inputs=outputs_anchors_div_input_size,
+        outputs=outputs_anchor_w + outputs_anchor_h,
+        axis=1,
+        split=[1, 1])
+    node_list.append(node_anchor_split)
+
+    new_anchor_shape = [1, int(num_anchors), 1, 1]
+    name_new_anchor_shape = [model_name + "@new_anchor_shape"]
+    node_new_anchor_shape = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_new_anchor_shape,
+        value=onnx.helper.make_tensor(
+            name=name_new_anchor_shape[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=[len(new_anchor_shape)],
+            vals=new_anchor_shape))
+    node_list.append(node_new_anchor_shape)
+
+    outputs_anchor_w_reshape = [model_name + "@anchor_w_reshape"]
+    outputs_anchor_h_reshape = [model_name + "@anchor_h_reshape"]
+
+    node_anchor_w_reshape = onnx.helper.make_node(
+        'Reshape',
+        inputs=outputs_anchor_w + name_new_anchor_shape,
+        outputs=outputs_anchor_w_reshape)
+    node_list.append(node_anchor_w_reshape)
+
+    node_anchor_h_reshape = onnx.helper.make_node(
+        'Reshape',
+        inputs=outputs_anchor_h + name_new_anchor_shape,
+        outputs=outputs_anchor_h_reshape)
+    node_list.append(node_anchor_h_reshape)
+
+    outputs_box_w_squeeze = [model_name + "@box_w_squeeze"]
+    node_box_w_squeeze = onnx.helper.make_node(
+        'Squeeze',
+        inputs=outputs_box_w,
+        outputs=outputs_box_w_squeeze,
+        axes=[4])
+    node_list.append(node_box_w_squeeze)
+
+    outputs_box_h_squeeze = [model_name + "@box_h_squeeze"]
+    node_box_h_squeeze = onnx.helper.make_node(
+        'Squeeze',
+        inputs=outputs_box_h,
+        outputs=outputs_box_h_squeeze,
+        axes=[4])
+    node_list.append(node_box_h_squeeze)
+
+    outputs_box_w_exp = [model_name + "@box_w_exp"]
+    node_box_w_exp = onnx.helper.make_node(
+        "Exp", inputs=outputs_box_w_squeeze, outputs=outputs_box_w_exp)
+    node_list.append(node_box_w_exp)
+
+    outputs_box_h_exp = [model_name + "@box_h_exp"]
+    node_box_h_exp = onnx.helper.make_node(
+        "Exp", inputs=outputs_box_h_squeeze, outputs=outputs_box_h_exp)
+    node_list.append(node_box_h_exp)
+
+    outputs_box_w_encode = [model_name + "box_w_encode"]
+    outputs_box_h_encode = [model_name + "box_h_encode"]
+
+    node_box_w_encode = onnx.helper.make_node(
+        'Mul',
+        inputs=outputs_box_w_exp + outputs_anchor_w_reshape,
+        outputs=outputs_box_w_encode)
+    node_list.append(node_box_w_encode)
+
+    node_box_h_encode = onnx.helper.make_node(
+        'Mul',
+        inputs=outputs_box_h_exp + outputs_anchor_h_reshape,
+        outputs=outputs_box_h_encode)
+    node_list.append(node_box_h_encode)
+
+    outputs_conf_sigmoid = [model_name + "@conf_sigmoid"]
+    node_conf_sigmoid = onnx.helper.make_node(
+        'Sigmoid', inputs=outputs_conf, outputs=outputs_conf_sigmoid)
+    node_list.append(node_conf_sigmoid)
+
+    name_conf_thresh = [model_name + "@conf_thresh"]
+    node_conf_thresh = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_conf_thresh,
+        value=onnx.helper.make_tensor(
+            name=name_conf_thresh[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=[num_anchors * input_height * input_width],
+            vals=conf_thresh_mat))
+    node_list.append(node_conf_thresh)
+
+    conf_shape = [1, int(num_anchors), input_height, input_width, 1]
+    name_conf_shape = [model_name + "@conf_shape"]
+    node_conf_shape = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_conf_shape,
+        value=onnx.helper.make_tensor(
+            name=name_conf_shape[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=[len(conf_shape)],
+            vals=conf_shape))
+    node_list.append(node_conf_shape)
+
+    outputs_conf_thresh_reshape = [model_name + "@conf_thresh_reshape"]
+    node_conf_thresh_reshape = onnx.helper.make_node(
+        'Reshape',
+        inputs=name_conf_thresh + name_conf_shape,
+        outputs=outputs_conf_thresh_reshape)
+    node_list.append(node_conf_thresh_reshape)
+
+    outputs_conf_sub = [model_name + "@conf_sub"]
+    node_conf_sub = onnx.helper.make_node(
+        'Sub',
+        inputs=outputs_conf_sigmoid + outputs_conf_thresh_reshape,
+        outputs=outputs_conf_sub)
+    node_list.append(node_conf_sub)
+
+    outputs_conf_clip = [model_name + "@conf_clip"]
+    node_conf_clip = onnx.helper.make_node(
+        'Clip', inputs=outputs_conf_sub, outputs=outputs_conf_clip)
+    node_list.append(node_conf_clip)
+
+    zeros = [0]
+    name_zeros = [model_name + "@zeros"]
+    node_zeros = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_zeros,
+        value=onnx.helper.make_tensor(
+            name=name_zeros[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=(),
+            vals=zeros))
+    node_list.append(node_zeros)
+
+    outputs_conf_clip_bool = [model_name + "@conf_clip_bool"]
+    node_conf_clip_bool = onnx.helper.make_node(
+        'Greater',
+        inputs=outputs_conf_clip + name_zeros,
+        outputs=outputs_conf_clip_bool)
+    node_list.append(node_conf_clip_bool)
+
+    outputs_conf_clip_cast = [model_name + "@conf_clip_cast"]
+    node_conf_clip_cast = onnx.helper.make_node(
+        'Cast',
+        inputs=outputs_conf_clip_bool,
+        outputs=outputs_conf_clip_cast,
+        to=1)
+    node_list.append(node_conf_clip_cast)
+
+    outputs_conf_set_zero = [model_name + "@conf_set_zero"]
+    node_conf_set_zero = onnx.helper.make_node(
+        'Mul',
+        inputs=outputs_conf_sigmoid + outputs_conf_clip_cast,
+        outputs=outputs_conf_set_zero)
+    node_list.append(node_conf_set_zero)
+
+    outputs_prob_sigmoid = [model_name + "@prob_sigmoid"]
+    node_prob_sigmoid = onnx.helper.make_node(
+        'Sigmoid', inputs=outputs_prob, outputs=outputs_prob_sigmoid)
+    node_list.append(node_prob_sigmoid)
+
+    new_shape = [1, int(num_anchors), input_height, input_width, 1]
+    name_new_shape = [model_name + "@new_shape"]
+    node_new_shape = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_new_shape,
+        value=onnx.helper.make_tensor(
+            name=name_new_shape[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=[len(new_shape)],
+            vals=new_shape))
+    node_list.append(node_new_shape)
+
+    outputs_conf_new_shape = [model_name + "@_conf_new_shape"]
+    node_conf_new_shape = onnx.helper.make_node(
+        'Reshape',
+        inputs=outputs_conf_set_zero + name_new_shape,
+        outputs=outputs_conf_new_shape)
+    node_list.append(node_conf_new_shape)
+
+    outputs_score = [model_name + "@score"]
+    node_score = onnx.helper.make_node(
+        'Mul',
+        inputs=outputs_prob_sigmoid + outputs_conf_new_shape,
+        outputs=outputs_score)
+    node_list.append(node_score)
+
+    outputs_conf_bool = [model_name + "@conf_bool"]
+    node_conf_bool = onnx.helper.make_node(
+        'Greater',
+        inputs=outputs_conf_new_shape + name_zeros,
+        outputs=outputs_conf_bool)
+    node_list.append(node_conf_bool)
+
+    outputs_box_x_new_shape = [model_name + "@box_x_new_shape"]
+    node_box_x_new_shape = onnx.helper.make_node(
+        'Reshape',
+        inputs=outputs_box_x_encode + name_new_shape,
+        outputs=outputs_box_x_new_shape)
+    node_list.append(node_box_x_new_shape)
+
+    outputs_box_y_new_shape = [model_name + "@box_y_new_shape"]
+    node_box_y_new_shape = onnx.helper.make_node(
+        'Reshape',
+        inputs=outputs_box_y_encode + name_new_shape,
+        outputs=outputs_box_y_new_shape)
+    node_list.append(node_box_y_new_shape)
+
+    outputs_box_w_new_shape = [model_name + "@box_w_new_shape"]
+    node_box_w_new_shape = onnx.helper.make_node(
+        'Reshape',
+        inputs=outputs_box_w_encode + name_new_shape,
+        outputs=outputs_box_w_new_shape)
+    node_list.append(node_box_w_new_shape)
+
+    outputs_box_h_new_shape = [model_name + "@box_h_new_shape"]
+    node_box_h_new_shape = onnx.helper.make_node(
+        'Reshape',
+        inputs=outputs_box_h_encode + name_new_shape,
+        outputs=outputs_box_h_new_shape)
+    node_list.append(node_box_h_new_shape)
+
+    outputs_pred_box = [model_name + "@pred_box"]
+    node_pred_box = onnx.helper.make_node(
+        'Concat',
+        inputs=outputs_box_x_new_shape + outputs_box_y_new_shape + \
+               outputs_box_w_new_shape + outputs_box_h_new_shape,
+        outputs=outputs_pred_box,
+        axis=4)
+    node_list.append(node_pred_box)
+
+    outputs_conf_cast = [model_name + "conf_cast"]
+    node_conf_cast = onnx.helper.make_node(
+        'Cast', inputs=outputs_conf_bool, outputs=outputs_conf_cast, to=1)
+    node_list.append(node_conf_cast)
+
+    outputs_pred_box_mul_conf = [model_name + "@pred_box_mul_conf"]
+    node_pred_box_mul_conf = onnx.helper.make_node(
+        'Mul',
+        inputs=outputs_pred_box + outputs_conf_cast,
+        outputs=outputs_pred_box_mul_conf)
+    node_list.append(node_pred_box_mul_conf)
+
+    box_shape = [1, int(num_anchors) * input_height * input_width, 4]
+    name_box_shape = [model_name + "@box_shape"]
+    node_box_shape = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_box_shape,
+        value=onnx.helper.make_tensor(
+            name=name_box_shape[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=[len(box_shape)],
+            vals=box_shape))
+    node_list.append(node_box_shape)
+
+    outputs_pred_box_new_shape = [model_name + "@pred_box_new_shape"]
+    node_pred_box_new_shape = onnx.helper.make_node(
+        'Reshape',
+        inputs=outputs_pred_box_mul_conf + name_box_shape,
+        outputs=outputs_pred_box_new_shape)
+    node_list.append(node_pred_box_new_shape)
+
+    outputs_pred_box_x = [model_name + "@_pred_box_x"]
+    outputs_pred_box_y = [model_name + "@_pred_box_y"]
+    outputs_pred_box_w = [model_name + "@_pred_box_w"]
+    outputs_pred_box_h = [model_name + "@_pred_box_h"]
+
+    node_pred_box_split = onnx.helper.make_node(
+        'Split',
+        inputs=outputs_pred_box_new_shape,
+        outputs=outputs_pred_box_x + outputs_pred_box_y + outputs_pred_box_w +
+        outputs_pred_box_h,
+        axis=2)
+    node_list.append(node_pred_box_split)
+
+    name_number_two = [model_name + "@number_two"]
+    node_number_two = onnx.helper.make_node(
+        "Constant",
+        inputs=[],
+        outputs=name_number_two,
+        value=onnx.helper.make_tensor(
+            name=name_number_two[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=(),
+            vals=[2]))
+    node_list.append(node_number_two)
+
+    outputs_half_w = [model_name + "@half_w"]
+    node_half_w = onnx.helper.make_node(
+        "Div",
+        inputs=outputs_pred_box_w + name_number_two,
+        outputs=outputs_half_w)
+    node_list.append(node_half_w)
+
+    outputs_half_h = [model_name + "@half_h"]
+    node_half_h = onnx.helper.make_node(
+        "Div",
+        inputs=outputs_pred_box_h + name_number_two,
+        outputs=outputs_half_h)
+    node_list.append(node_half_h)
+
+    outputs_pred_box_x1 = [model_name + "@pred_box_x1"]
+    node_pred_box_x1 = onnx.helper.make_node(
+        'Sub',
+        inputs=outputs_pred_box_x + outputs_half_w,
+        outputs=outputs_pred_box_x1)
+    node_list.append(node_pred_box_x1)
+
+    outputs_pred_box_y1 = [model_name + "@pred_box_y1"]
+    node_pred_box_y1 = onnx.helper.make_node(
+        'Sub',
+        inputs=outputs_pred_box_y + outputs_half_h,
+        outputs=outputs_pred_box_y1)
+    node_list.append(node_pred_box_y1)
+
+    outputs_pred_box_x2 = [model_name + "@pred_box_x2"]
+    node_pred_box_x2 = onnx.helper.make_node(
+        'Add',
+        inputs=outputs_pred_box_x + outputs_half_w,
+        outputs=outputs_pred_box_x2)
+    node_list.append(node_pred_box_x2)
+
+    outputs_pred_box_y2 = [model_name + "@pred_box_y2"]
+    node_pred_box_y2 = onnx.helper.make_node(
+        'Add',
+        inputs=outputs_pred_box_y + outputs_half_h,
+        outputs=outputs_pred_box_y2)
+    node_list.append(node_pred_box_y2)
+
+    outputs_sqeeze_image_size = [model_name + "@sqeeze_image_size"]
+    node_sqeeze_image_size = onnx.helper.make_node(
+        "Squeeze",
+        axes=[0],
+        inputs=image_size,
+        outputs=outputs_sqeeze_image_size)
+    node_list.append(node_sqeeze_image_size)
+
+    output_img_height = [model_name + "@img_height"]
+    output_img_width = [model_name + "@img_width"]
+    node_image_size_split = onnx.helper.make_node(
+        "Split",
+        inputs=outputs_sqeeze_image_size,
+        outputs=output_img_height + output_img_width,
+        axis=-1,
+        split=[1, 1])
+    node_list.append(node_image_size_split)
+
+    output_img_width_cast = [model_name + "@img_width_cast"]
+    node_img_width_cast = onnx.helper.make_node(
+        'Cast', inputs=output_img_width, outputs=output_img_width_cast, to=1)
+    node_list.append(node_img_width_cast)
+
+    output_img_height_cast = [model_name + "@img_height_cast"]
+    node_img_height_cast = onnx.helper.make_node(
+        'Cast', inputs=output_img_height, outputs=output_img_height_cast, to=1)
+    node_list.append(node_img_height_cast)
+
+    outputs_pred_box_x1_decode = [model_name + "@pred_box_x1_decode"]
+    outputs_pred_box_y1_decode = [model_name + "@pred_box_y1_decode"]
+    outputs_pred_box_x2_decode = [model_name + "@pred_box_x2_decode"]
+    outputs_pred_box_y2_decode = [model_name + "@pred_box_y2_decode"]
+
+    node_pred_box_x1_decode = onnx.helper.make_node(
+        'Mul',
+        inputs=outputs_pred_box_x1 + output_img_width_cast,
+        outputs=outputs_pred_box_x1_decode)
+    node_list.append(node_pred_box_x1_decode)
+
+    node_pred_box_y1_decode = onnx.helper.make_node(
+        'Mul',
+        inputs=outputs_pred_box_y1 + output_img_height_cast,
+        outputs=outputs_pred_box_y1_decode)
+    node_list.append(node_pred_box_y1_decode)
+
+    node_pred_box_x2_decode = onnx.helper.make_node(
+        'Mul',
+        inputs=outputs_pred_box_x2 + output_img_width_cast,
+        outputs=outputs_pred_box_x2_decode)
+    node_list.append(node_pred_box_x2_decode)
+
+    node_pred_box_y2_decode = onnx.helper.make_node(
+        'Mul',
+        inputs=outputs_pred_box_y2 + output_img_height_cast,
+        outputs=outputs_pred_box_y2_decode)
+    node_list.append(node_pred_box_y2_decode)
+
+    name_number_one = [model_name + "@one"]
+    node_number_one = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=name_number_one,
+        value=onnx.helper.make_tensor(
+            name=name_number_one[0] + "@const",
+            data_type=onnx.TensorProto.FLOAT,
+            dims=(),
+            vals=[1]))
+    node_list.append(node_number_one)
+
+    output_new_img_height = [model_name + "@new_img_height"]
+    node_new_img_height = onnx.helper.make_node(
+        'Sub',
+        inputs=output_img_height_cast + name_number_one,
+        outputs=output_new_img_height)
+    node_list.append(node_new_img_height)
+
+    output_new_img_width = [model_name + "@new_img_width"]
+    node_new_img_width = onnx.helper.make_node(
+        'Sub',
+        inputs=output_img_width_cast + name_number_one,
+        outputs=output_new_img_width)
+    node_list.append(node_new_img_width)
+
+    outputs_pred_box_x2_sub_w = [model_name + "@pred_box_x2_sub_w"]
+    node_pred_box_x2_sub_w = onnx.helper.make_node(
+        'Sub',
+        inputs=outputs_pred_box_x2_decode + output_new_img_width,
+        outputs=outputs_pred_box_x2_sub_w)
+    node_list.append(node_pred_box_x2_sub_w)
+
+    outputs_pred_box_y2_sub_h = [model_name + "@pred_box_y2_sub_h"]
+    node_pred_box_y2_sub_h = onnx.helper.make_node(
+        'Sub',
+        inputs=outputs_pred_box_y2_decode + output_new_img_height,
+        outputs=outputs_pred_box_y2_sub_h)
+    node_list.append(node_pred_box_y2_sub_h)
+
+    outputs_pred_box_x1_clip = [model_name + "@pred_box_x1_clip"]
+    outputs_pred_box_y1_clip = [model_name + "@pred_box_y1_clip"]
+    outputs_pred_box_x2_clip = [model_name + "@pred_box_x2_clip"]
+    outputs_pred_box_y2_clip = [model_name + "@pred_box_y2_clip"]
+
+    node_pred_box_x1_clip = onnx.helper.make_node(
+        'Clip',
+        inputs=outputs_pred_box_x1_decode,
+        outputs=outputs_pred_box_x1_clip,
+        min=0.0,
+        max=float(np.inf))
+    node_list.append(node_pred_box_x1_clip)
+
+    node_pred_box_y1_clip = onnx.helper.make_node(
+        'Clip',
+        inputs=outputs_pred_box_y1_decode,
+        outputs=outputs_pred_box_y1_clip,
+        min=0.0,
+        max=float(np.inf))
+    node_list.append(node_pred_box_y1_clip)
+
+    node_pred_box_x2_clip = onnx.helper.make_node(
+        'Clip',
+        inputs=outputs_pred_box_x2_sub_w,
+        outputs=outputs_pred_box_x2_clip,
+        min=0.0,
+        max=float(np.inf))
+    node_list.append(node_pred_box_x2_clip)
+
+    node_pred_box_y2_clip = onnx.helper.make_node(
+        'Clip',
+        inputs=outputs_pred_box_y2_sub_h,
+        outputs=outputs_pred_box_y2_clip,
+        min=0.0,
+        max=float(np.inf))
+    node_list.append(node_pred_box_y2_clip)
+
+    outputs_pred_box_x2_res = [model_name + "@box_x2_res"]
+    node_pred_box_x2_res = onnx.helper.make_node(
+        'Sub',
+        inputs=outputs_pred_box_x2_decode + outputs_pred_box_x2_clip,
+        outputs=outputs_pred_box_x2_res)
+    node_list.append(node_pred_box_x2_res)
+
+    outputs_pred_box_y2_res = [model_name + "@box_y2_res"]
+    node_pred_box_y2_res = onnx.helper.make_node(
+        'Sub',
+        inputs=outputs_pred_box_y2_decode + outputs_pred_box_y2_clip,
+        outputs=outputs_pred_box_y2_res)
+    node_list.append(node_pred_box_y2_res)
+
+    node_pred_box_result = onnx.helper.make_node(
+        'Concat',
+        inputs=outputs_pred_box_x1_clip + outputs_pred_box_y1_clip +
+        outputs_pred_box_x2_res + outputs_pred_box_y2_res,
+        outputs=outputs['Boxes'],
+        axis=-1)
+    node_list.append(node_pred_box_result)
+
+    score_shape = [1, input_height * input_width * int(num_anchors), class_num]
+    name_score_shape = [model_name + "@score_shape"]
+    node_score_shape = onnx.helper.make_node(
+        "Constant",
+        inputs=[],
+        outputs=name_score_shape,
+        value=onnx.helper.make_tensor(
+            name=name_score_shape[0] + "@const",
+            data_type=onnx.TensorProto.INT64,
+            dims=[len(score_shape)],
+            vals=score_shape))
+    node_list.append(node_score_shape)
+
+    node_score_new_shape = onnx.helper.make_node(
+        'Reshape',
+        inputs=outputs_score + name_score_shape,
+        outputs=outputs['Scores'])
+    node_list.append(node_score_new_shape)
+    return node_list

x2paddle/op_mapper/paddle_op_mapper.py

+#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import sys
+import x2paddle
+import os
+import numpy as np
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+import onnx
+from onnx import helper, onnx_pb
+
+
+class PaddleOpMapper(object):
+    def __init__(self):
+        self.paddle_onnx_dtype_map = {
+            core.VarDesc.VarType.FP32: onnx_pb.TensorProto.FLOAT,
+            core.VarDesc.VarType.FP64: onnx_pb.TensorProto.DOUBLE,
+            core.VarDesc.VarType.INT32: onnx_pb.TensorProto.INT32,
+            core.VarDesc.VarType.INT16: onnx_pb.TensorProto.INT16,
+            core.VarDesc.VarType.INT16: onnx_pb.TensorProto.UINT16,
+            core.VarDesc.VarType.INT64: onnx_pb.TensorProto.INT64,
+            core.VarDesc.VarType.BOOL: onnx_pb.TensorProto.BOOL
+        }
+
+        self.name_counter = dict()
+
+    def convert(self, program, save_dir):
+        weight_nodes = self.convert_weights(program)
+        op_nodes = list()
+        input_nodes = list()
+        output_nodes = list()
+
+        unsupported_ops = set()
+
+        print("Translating PaddlePaddle to ONNX...\n")
+        for block in program.blocks:
+            for i, op in enumerate(block.ops):
+                sys.stdout.write(
+                    "\rTotal:{}, Current:{} : {}                   ".format(
+                        len(block.ops), i + 1, op.type))
+                sys.stdout.flush()
+                if not hasattr(self, op.type):
+                    unsupported_ops.add(op.type)
+                    continue
+                if len(unsupported_ops) > 0:
+                    continue
+                node = getattr(self, op.type)(op, block)
+                if op.type == 'feed':
+                    input_nodes.append(node)
+                elif op.type == 'fetch':
+                    output_nodes.append(node)
+                else:
+                    if isinstance(node, list):
+                        op_nodes = op_nodes + node
+                    else:
+                        op_nodes.append(node)
+
+        if len(unsupported_ops) > 0:
+            print("\nThere's {} ops are not supported yet".format(
+                len(unsupported_ops)))
+            for op in unsupported_ops:
+                print("=========== {} ===========".format(op))
+            return
+
+        graph = helper.make_graph(
+            nodes=weight_nodes + op_nodes,
+            name='onnx_model_from_paddle',
+            initializer=[],
+            inputs=input_nodes,
+            outputs=output_nodes)
+        model = helper.make_model(graph, producer_name='X2Paddle')
+        onnx.checker.check_model(model)
+
+        if not os.path.isdir(save_dir):
+            os.makedirs(save_dir)
+        with open(os.path.join(save_dir, 'x2paddle_model.onnx'), 'wb') as f:
+            f.write(model.SerializeToString())
+        print("\nTranslated model saved in {}".format(
+            os.path.join(save_dir, 'x2paddle_model.onnx')))
+
+    def get_name(self, op_name, var_name):
+        name = 'p2o.{}.{}'.format(op_name, var_name)
+        if name not in self.name_counter:
+            self.name_counter[name] = 0
+        else:
+            self.name_counter[name] += 1
+        return name + '.{}'.format(self.name_counter[name])
+
+    def convert_weights(self, program):
+        var_names = program.global_block().vars
+        nodes = list()
+        for name in var_names:
+            var = program.global_block().var(name)
+            if name.endswith('feed') or name.endswith('fetch'):
+                continue
+            if not var.persistable:
+                continue
+            weight = np.array(fluid.global_scope().find_var(name).get_tensor())
+            tensor = helper.make_tensor(
+                name=name,
+                dims=var.shape,
+                data_type=self.paddle_onnx_dtype_map[var.dtype],
+                vals=weight.flatten().tolist())
+            node = helper.make_node(
+                'Constant', inputs=[], outputs=[name], value=tensor)
+            nodes.append(node)
+        return nodes
+
+    def make_constant_node(self, name, dtype, value=None):
+        if isinstance(value, list):
+            dims = (len(value), )
+        elif value is None:
+            dims = ()
+            value = []
+        else:
+            dims = ()
+            value = [value]
+        tensor = helper.make_tensor(
+            name=name, data_type=dtype, dims=dims, vals=value)
+        node = helper.make_node(
+            'Constant', inputs=[], outputs=[name], value=tensor)
+        return node
+
+    def conv2d(self, op, block):
+        kernel_shape = block.var(op.input('Filter')[0]).shape
+        node = helper.make_node(
+            'Conv',
+            inputs=op.input('Input') + op.input('Filter'),
+            outputs=op.output('Output'),
+            dilations=op.attr('dilations'),
+            kernel_shape=kernel_shape[-2:],
+            strides=op.attr('strides'),
+            group=op.attr('groups'),
+            pads=op.attr('paddings') + op.attr('paddings'))
+        return node
+
+    def conv2d_transpose(self, op, block):
+        kernel_shape = block.var(op.input('Filter')[0]).shape
+        node = helper.make_node(
+            'ConvTranspose',
+            inputs=op.input('Input') + op.input('Filter'),
+            outputs=op.output('Output'),
+            dilations=op.attr('dilations'),
+            kernel_shape=kernel_shape[-2:],
+            strides=op.attr('strides'),
+            group=1,
+            pads=op.attr('paddings') + op.attr('paddings'))
+        return node
+
+    def relu(self, op, block):
+        node = helper.make_node(
+            'Relu', inputs=op.input('X'), outputs=op.output('Out'))
+        return node
+
+    def sigmoid(self, op, block):
+        node = helper.make_node(
+            'Sigmoid', inputs=op.input('X'), outputs=op.output('Out'))
+        return node
+
+    def exp(self, op, block):
+        node = helper.make_node(
+            'Exp', inputs=op.input('X'), outputs=op.output('Out'))
+        return node
+
+    def leaky_relu(self, op, block):
+        node = helper.make_node(
+            'LeakyRelu',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            alpha=op.attr('alpha'))
+        return node
+
+    def elementwise_add(self, op, block):
+        axis = op.attr('axis')
+        x_shape = block.var(op.input('X')[0]).shape
+        y_shape = block.var(op.input('Y')[0]).shape
+        if len(y_shape) == 1 and axis == 1:
+            shape_name = self.get_name(op.type, 'shape')
+            shape_value = [1] * len(x_shape)
+            shape_value[axis] = y_shape[0]
+            shape_node = self.make_constant_node(
+                shape_name, onnx_pb.TensorProto.INT64, shape_value)
+            temp_value = self.get_name(op.type, 'temp')
+            y_node = helper.make_node(
+                'Reshape',
+                inputs=[op.input('Y')[0], shape_name],
+                outputs=[temp_value])
+            node = helper.make_node(
+                'Add',
+                inputs=[op.input('X')[0], temp_value],
+                outputs=op.output('Out'))
+            return [shape_node, y_node, node]
+        elif len(x_shape) == len(y_shape):
+            node = helper.make_node(
+                'Add',
+                inputs=[op.input('X')[0], op.input('Y')[0]],
+                outputs=op.output('Out'))
+            return node
+        else:
+            raise Excpetion("Unexpected situation happend in elementwise_add")
+
+    def elementwise_sub(self, op, block):
+        axis = op.attr('axis')
+        x_shape = block.var(op.input('X')[0]).shape
+        y_shape = block.var(op.input('Y')[0]).shape
+        if len(y_shape) == 1 and axis == 1:
+            shape_name = self.get_name(op.type, 'shape')
+            shape_value = [1] * len(x_shape)
+            shape_value[axis] = y_shape[0]
+            shape_node = self.make_constant_node(
+                shape_name, onnx_pb.TensorProto.INT64, shape_value)
+            temp_value = self.get_name(op.type, 'temp')
+            y_node = helper.make_node(
+                'Reshape',
+                inputs=[op.input('Y')[0], shape_name],
+                outputs=[temp_value])
+            node = helper.make_node(
+                'Sub',
+                inputs=[op.input('X')[0], temp_value],
+                outputs=op.output('Out'))
+            return [shape_node, y_node, node]
+        elif len(x_shape) == len(y_shape):
+            node = helper.make_node(
+                'Sub',
+                inputs=[op.input('X')[0], op.input('Y')[0]],
+                outputs=op.output('Out'))
+            return node
+        else:
+            raise Excpetion("Unexpected situation happend in elementwise_sub")
+
+    def pool2d(self, op, block):
+        pool_type = {
+            'max': ('MaxPool', 'GlobalMaxPool'),
+            'avg': ('AveragePool', 'GlobalAveragePool')
+        }
+        if op.attr('global_pooling'):
+            node = helper.make_node(
+                pool_type[op.attr('pooling_type')][1],
+                inputs=op.input('X'),
+                outputs=op.output('Out'), )
+        else:
+            input_shape = block.var(op.input('X')[0]).shape
+            k_size = op.attr('ksize')
+            paddings = op.attr('paddings')
+            if input_shape[2] > 0 and input_shape[2] + paddings[0] < k_size[0]:
+                k_size[0] = input_shape[2] + paddings[0]
+            if input_shape[3] > 0 and input_shape[3] + paddings[1] < k_size[1]:
+                k_size[1] = input_shape[3] + paddings[1]
+            node = helper.make_node(
+                pool_type[op.attr('pooling_type')][0],
+                inputs=op.input('X'),
+                outputs=op.output('Out'),
+                kernel_shape=k_size,
+                strides=op.attr('strides'),
+                pads=op.attr('paddings') + op.attr('paddings'))
+        return node
+
+    def softmax(self, op, block):
+        axis = op.attr('axis')
+        shape = block.var(op.output('Out')[0]).shape
+        if axis < 0:
+            axis += len(shape)
+        if axis == len(shape) - 1:
+            node = helper.make_node(
+                'Softmax',
+                inputs=op.input('X'),
+                outputs=op.output('Out'),
+                axis=op.attr('axis'))
+            return node
+        else:
+            perm = [i for i in range(len(shape))]
+            perm[-1] = axis
+            perm[axis] = len(shape) - 1
+            transpose_name0 = self.get_name(op.type, 'transpose')
+            transpose_node0 = helper.make_node(
+                'Transpose',
+                inputs=op.input('X'),
+                outputs=[transpose_name0],
+                perm=perm)
+            softmax_name = self.get_name(op.type, 'softmax')
+            softmax_node = helper.make_node(
+                'Softmax',
+                inputs=[transpose_name0],
+                outputs=[softmax_name],
+                axis=-1)
+            transpose_name1 = self.get_name(op.type, 'transpose')
+            transpose_node1 = helper.make_node(
+                'Transpose',
+                inputs=[softmax_name],
+                outputs=op.output('Out'),
+                perm=perm)
+            return [transpose_node0, softmax_node, transpose_node1]
+
+    def scale(self, op, block):
+        scale = op.attr('scale')
+        bias = op.attr('bias')
+        if math.fabs(scale - 1.0) < 1e-06 and math.fabs(bias - 0.0) < 1e-06:
+            node = helper.make_node(
+                'Identity', inputs=op.input('X'), outputs=op.output('Out'))
+            return node
+        else:
+            scale_name = self.get_name(op.type, 'scale')
+            bias_name = self.get_name(op.type, 'bias')
+            scale_node = self.make_constant_node(
+                scale_name, onnx_pb.TensorProto.FLOAT, scale)
+            bias_node = self.make_constant_node(bias_name,
+                                                onnx_pb.TensorProto.FLOAT, bias)
+            temp_tensor_name = self.get_name(op.type, 'temporary')
+            if op.attr('bias_after_scale'):
+                node1 = helper.make_node(
+                    'Mul',
+                    inputs=[scale_name, op.input('X')[0]],
+                    outputs=[temp_tensor_name])
+                node2 = helper.make_node(
+                    'Add',
+                    inputs=[bias_name, temp_tensor_name],
+                    outputs=op.output('Out'))
+            else:
+                node1 = helper.make_node(
+                    'Add',
+                    inputs=[bias_name, op.input('X')[0]],
+                    outputs=temp_tensor_name)
+                node2 = helper.make_node(
+                    'Mul',
+                    inputs=[scale_name, temp_tensor_name],
+                    outputs=[op.output('Out')])
+            return [scale_node, bias_node, node1, node2]
+
+    def mul(self, op, block):
+        x_shape = block.var(op.input('X')[0]).shape
+        y_shape = block.var(op.input('Y')[0]).shape
+        out_shape = list(block.var(op.output('Out')[0]).shape)
+        x_num_col_dims = op.attr('x_num_col_dims')
+        y_num_col_dims = op.attr('y_num_col_dims')
+        flatten_x_name = 'flatten_{}'.format(op.input('X')[0])
+        flatten_y_name = 'flatten_{}'.format(op.input('Y')[0])
+        shape_name = 'temp_shape_{}'.format(op.output('Out')[0])
+        temp_out_name = 'temp_{}'.format(op.output('Out')[0])
+        flatten_x = helper.make_node(
+            'Flatten',
+            inputs=op.input('X'),
+            outputs=[flatten_x_name],
+            axis=x_num_col_dims)
+        flatten_y = helper.make_node(
+            'Flatten',
+            inputs=op.input('Y'),
+            outputs=[flatten_y_name],
+            axis=y_num_col_dims)
+        shape_node = self.make_constant_node(
+            shape_name, onnx_pb.TensorProto.INT64, out_shape)
+        node = helper.make_node(
+            'MatMul',
+            inputs=[flatten_x_name, flatten_y_name],
+            outputs=[temp_out_name])
+        reshape_out = helper.make_node(
+            'Reshape',
+            inputs=[temp_out_name, shape_name],
+            outputs=op.output('Out'))
+        return [flatten_x, flatten_y, shape_node, node, reshape_out]
+
+    def batch_norm(self, op, block):
+        kwargs = {
+            'epsilon': op.attr('epsilon'),
+            'momentum': op.attr('momentum')
+        }
+        inputs = op.input('X') + op.input('Scale') + op.input(
+            'Bias') + op.input('Mean') + op.input('Variance')
+        node = helper.make_node(
+            'BatchNormalization',
+            inputs=inputs,
+            outputs=op.output('Y'),
+            **kwargs)
+        return node
+
+    def concat(self, op, block):
+        node = helper.make_node(
+            'Concat',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            axis=op.attr('axis'))
+        return node
+
+    def depthwise_conv2d(self, op, block):
+        return self.conv2d(op, block)
+
+    def relu6(self, op, block):
+        min_name = self.get_name(op.type, 'min')
+        max_name = self.get_name(op.type, 'max')
+        min_node = self.make_constant_node(min_name, onnx_pb.TensorProto.FLOAT,
+                                           0)
+        max_node = self.make_constant_node(max_name, onnx_pb.TensorProto.FLOAT,
+                                           op.attr('threshold'))
+        node = helper.make_node(
+            'Clip',
+            inputs=[op.input('X')[0], min_name, max_name],
+            outputs=op.output('Out'), )
+        return [min_node, max_node, node]
+
+    def shape(self, op, block):
+        node = helper.make_node(
+            'Shape', inputs=op.input('Input'), outputs=op.output('Out'))
+        return node
+
+    def split(self, op, block):
+        sections = op.attr('sections')
+        if len(sections) > 0:
+            node = helper.make_node(
+                'Split',
+                inputs=op.input('X'),
+                outputs=op.output('Out'),
+                axis=op.attr('axis'),
+                split=sections)
+        else:
+            node = helper.make_node(
+                'Split',
+                inputs=op.input('X'),
+                outputs=op.output('Out'),
+                axis=op.attr('axis'))
+        return node
+
+    def slice(self, op, block):
+        axes = op.attr('axes')
+        starts = op.attr('starts')
+        ends = op.attr('ends')
+        axes_name = self.get_name(op.type, 'axes')
+        starts_name = self.get_name(op.type, 'starts')
+        ends_name = self.get_name(op.type, 'ends')
+
+        axes_node = self.make_constant_node(axes_name,
+                                            onnx_pb.TensorProto.INT64, axes)
+        starts_node = self.make_constant_node(starts_name,
+                                              onnx_pb.TensorProto.INT64, starts)
+        ends_node = self.make_constant_node(ends_name,
+                                            onnx_pb.TensorProto.INT64, ends)
+        node = helper.make_node(
+            "Slice",
+            inputs=[op.input('Input')[0], starts_name, ends_name, axes_name],
+            outputs=op.output('Out'), )
+        return [starts_node, ends_node, axes_node, node]
+
+    def fill_constant(self, op, block):
+        value = op.attr('value')
+        dtype = op.attr('dtype')
+        shape = op.attr('shape')
+        value = np.ones(shape) * value
+        if dtype == 2:
+            value = value.astype('int32')
+        node = helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=op.output('Out'),
+            value=helper.make_tensor(
+                name=op.output('Out')[0],
+                data_type=self.paddle_onnx_dtype_map[dtype],
+                dims=shape,
+                vals=value.tolist()))
+        return node
+
+    def transpose2(self, op, block):
+        node = helper.make_node(
+            'Transpose',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            perm=op.attr('axis'))
+        return node
+
+    def reshape2(self, op, block):
+        input_names = op.input_names
+        if len(op.input('ShapeTensor')) > 1:
+            cast_shape_nodes = list()
+            cast_shape_names = list()
+            for i in range(len(op.input('ShapeTensor'))):
+                dim = op.input('ShapeTensor')[i]
+                temp_name = self.get_name(op.type, 'shape.cast')
+                node = helper.make_node(
+                    'Cast',
+                    inputs=[dim],
+                    outputs=[temp_name],
+                    to=onnx_pb.TensorProto.INT64)
+                cast_shape_nodes.append(node)
+                cast_shape_names.append(temp_name)
+
+            temp_name = self.get_name(op.type, 'shape.concat')
+            shape_node = helper.make_node(
+                'Concat', inputs=cast_shape_names, outputs=[temp_name], axis=-1)
+            node = helper.make_node(
+                'Reshape',
+                inputs=[op.input('X')[0], temp_name],
+                outputs=op.output('Out'))
+            return cast_shape_nodes + [shape_node, node]
+        else:
+            temp_name = self.get_name(op.type, 'shape.cast')
+            cast_shape_node = helper.make_node(
+                'Cast',
+                inputs=op.input('ShapeTensor'),
+                outputs=[temp_name],
+                to=onnx_pb.TensorProto.INT64)
+            node = helper.make_node(
+                'Reshape',
+                inputs=[op.input('X')[0], temp_name],
+                outputs=op.output('Out'))
+            return [cast_shape_node, node]
+
+    def dropout(self, op, block):
+        dropout_mode = op.attr('dropout_implementation')
+        dropout_prob = op.attr('dropout_prob')
+        if dropout_mode == 'upscale_in_train':
+            node = helper.make_node(
+                'Identity', inputs=op.input('X'), outputs=op.output('Out'))
+            return node
+        elif dropout_mode == 'downgrade_in_infer':
+            scale_name = self.get_name(op.type, 'scale')
+            scale_node = self.make_constant_node(
+                scale_name, onnx_pb.TensorProto.FLOAT, 1 - dropout_prob)
+            node = helper.make_node(
+                "Mul",
+                inputs=[op.input('X')[0], scale_name],
+                outputs=op.output('Out'))
+            return [scale_node, node]
+        else:
+            raise Exception("Unexpected situation happend")
+
+    def reduce_mean(self, op, block):
+        node = helper.make_node(
+            'ReduceMean',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            axes=op.attr('dim'),
+            keepdims=op.attr('keep_dim'))
+        return node
+
+    def bilinear_interp(self, op, block):
+        input_names = op.input_names
+        coordinate_transformation_mode = 'half_pixel'
+        if op.attr('align_corners'):
+            coordinate_transformation_mode = 'align_corners'
+        if ('OutSize' in input_names and len(op.input('OutSize')) > 0) or (
+                'SizeTensor' in input_names and
+                len(op.input('SizeTensor')) > 0):
+            node_list = list()
+            roi_node = self.make_constant_node(
+                self.get_name(op.type, 'roi'), onnx_pb.TensorProto.FLOAT,
+                [1, 1, 1, 1, 1, 1, 1, 1])
+            roi_name = self.get_name(op.type, 'roi')
+            roi_node = self.make_constant_node(
+                roi_name, onnx_pb.TensorProto.FLOAT, [1, 1, 1, 1, 1, 1, 1, 1])
+            empty_name = self.get_name(op.type, 'empty')
+            empty_tensor = helper.make_tensor(
+                empty_name,
+                onnx_pb.TensorProto.FLOAT, (0, ),
+                np.array([]).astype('float32'),
+                raw=False)
+            empty_node = helper.make_node(
+                'Constant', [], outputs=[empty_name], value=empty_tensor)
+            shape_name0 = self.get_name(op.type, 'shape')
+            shape_node0 = helper.make_node(
+                'Shape', inputs=op.input('X'), outputs=[shape_name0])
+            starts_name = self.get_name(op.type, 'slice.starts')
+            starts_node = self.make_constant_node(
+                starts_name, onnx_pb.TensorProto.INT64, [0])
+            ends_name = self.get_name(op.type, 'slice.ends')
+            ends_node = self.make_constant_node(ends_name,
+                                                onnx_pb.TensorProto.INT64, [2])
+            shape_name1 = self.get_name(op.type, 'shape')
+            shape_node1 = helper.make_node(
+                'Slice',
+                inputs=[shape_name0, starts_name, ends_name],
+                outputs=[shape_name1])
+            node_list.extend([
+                roi_node, empty_node, shape_node0, starts_node, ends_node,
+                shape_node1
+            ])
+            #            shape_name2 = self.get_name(op.type, "shape.cast")
+            #            shape_node2 = helper.make_node(
+            #                'Cast',
+            #                inputs=op.input('OutSize'),
+            #                outputs=[shape_name2],
+            #                to=onnx_pb.TensorProto.INT64)
+            if 'OutSize' in input_names and len(op.input('OutSize')) > 0:
+                cast_shape_name = self.get_name(op.type, "shape.cast")
+                cast_shape_node = helper.make_node(
+                    'Cast',
+                    inputs=op.input('OutSize'),
+                    outputs=[cast_shape_name],
+                    to=onnx_pb.TensorProto.INT64)
+                node_list.append(cast_shape_node)
+            else:
+                concat_shape_name = self.get_name(op.type, "shape.concat")
+                concat_shape_node = helper.make_node(
+                    "Concat",
+                    inputs=op.input('SizeTensor'),
+                    outputs=[concat_shape_name],
+                    axis=0)
+                cast_shape_name = self.get_name(op.type, "shape.cast")
+                cast_shape_node = helper.make_node(
+                    'Cast',
+                    inputs=[concat_shape_name],
+                    outputs=[cast_shape_name],
+                    to=onnx_pb.TensorProto.INT64)
+                node_list.extend([concat_shape_node, cast_shape_node])
+            shape_name3 = self.get_name(op.type, "shape.concat")
+            shape_node3 = helper.make_node(
+                'Concat',
+                inputs=[shape_name1, cast_shape_name],
+                outputs=[shape_name3],
+                axis=0)
+            result_node = helper.make_node(
+                'Resize',
+                inputs=[op.input('X')[0], roi_name, empty_name, shape_name3],
+                outputs=op.output('Out'),
+                mode='linear',
+                coordinate_transformation_mode=coordinate_transformation_mode)
+            node_list.extend([shape_node3, result_node])
+            return node_list
+        elif 'Scale' in input_names and len(op.input('Scale')) > 0:
+            node = helper.make_node(
+                'Resize',
+                inputs=[op.input('X')[0], op.input('Scale')[0]],
+                outputs=op.output('Out'),
+                mode='linear',
+                coordinate_transformation_mode=coordinate_transformation_mode)
+        else:
+            out_shape = [op.attr('out_h'), op.attr('out_w')]
+            scale = op.attr('scale')
+            if out_shape.count(-1) > 0:
+                scale_name = self.get_name(op.type, 'scale')
+                scale_node = self.make_constant_node(scale_name,
+                                                     onnx_pb.TensorProto.FLOAT,
+                                                     [1, 1, scale, scale])
+                roi_name = self.get_name(op.type, 'roi')
+                roi_node = self.make_constant_node(roi_name,
+                                                   onnx_pb.TensorProto.FLOAT,
+                                                   [1, 1, 1, 1, 1, 1, 1, 1])
+                node = helper.make_node(
+                    'Resize',
+                    inputs=[op.input('X')[0], roi_name, scale_name],
+                    outputs=op.output('Out'),
+                    mode='nearest',
+                    coordinate_transformation_mode=coordinate_transformation_mode
+                )
+                return [scale_node, roi_node, node]
+            else:
+                raise Exception("Unexpected situation happend")
+        return node
+
+    def nearest_interp(self, op, block):
+        input_names = op.input_names
+        coordinate_transformation_mode = 'half_pixel'
+        if op.attr('align_corners'):
+            coordinate_transformation_mode = 'align_corners'
+        if 'OutSize' in input_names and len(op.input('OutSize')) > 0:
+            node = helper.make_node(
+                'Resize',
+                inputs=[op.input('X')[0], '', op.input('OutSize')[0]],
+                outputs=op.output('Out'),
+                mode='nearest',
+                coordinate_transformation_mode=coordinate_transformation_mode)
+        elif 'Scale' in input_names and len(op.input('Scale')) > 0:
+            node = helper.make_node(
+                'Resize',
+                inputs=[op.input('X')[0], op.input('Scale')[0]],
+                outputs=op.output('Out'),
+                mode='nearest',
+                coordinate_transformation_mode=coordinate_transformation_mode)
+        else:
+            out_shape = [op.attr('out_h'), op.attr('out_w')]
+            scale = op.attr('scale')
+            if out_shape.count(-1) > 0:
+                scale_name = self.get_name(op.type, 'scale')
+                scale_node = self.make_constant_node(scale_name,
+                                                     onnx_pb.TensorProto.FLOAT,
+                                                     [1, 1, scale, scale])
+                roi_name = self.get_name(op.type, 'roi')
+                roi_node = self.make_constant_node(roi_name,
+                                                   onnx_pb.TensorProto.FLOAT,
+                                                   [1, 1, 1, 1, 1, 1, 1, 1])
+                node = helper.make_node(
+                    'Resize',
+                    inputs=[op.input('X')[0], roi_name, scale_name],
+                    outputs=op.output('Out'),
+                    mode='nearest',
+                    coordinate_transformation_mode=coordinate_transformation_mode
+                )
+                return [scale_node, roi_node, node]
+            else:
+                raise Exception("Unexpected situation happend")
+        return node
+
+    def hard_sigmoid(self, op, block):
+        slope = op.attr('slope')
+        offset = op.attr('offset')
+        node = helper.make_node(
+            'HardSigmoid',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            alpha=slope,
+            beta=offset)
+        return node
+
+    def hard_swish(self, op, block):
+        min_name = self.get_name(op.type, 'min')
+        max_name = self.get_name(op.type, 'max')
+        scale_name = self.get_name(op.type, 'scale')
+        offset_name = self.get_name(op.type, 'offset')
+        min_node = self.make_constant_node(min_name, onnx_pb.TensorProto.FLOAT,
+                                           0)
+        max_node = self.make_constant_node(max_name, onnx_pb.TensorProto.FLOAT,
+                                           op.attr('threshold'))
+        scale_node = self.make_constant_node(scale_name,
+                                             onnx_pb.TensorProto.FLOAT,
+                                             op.attr('scale'))
+        offset_node = self.make_constant_node(offset_name,
+                                              onnx_pb.TensorProto.FLOAT,
+                                              op.attr('offset'))
+
+        name0 = self.get_name(op.type, 'add')
+        node0 = helper.make_node(
+            'Add', inputs=[op.input('X')[0], offset_name], outputs=[name0])
+        name1 = self.get_name(op.type, 'relu')
+        node1 = helper.make_node(
+            'Clip',
+            inputs=[name0, min_name, max_name],
+            outputs=[name1], )
+        name2 = self.get_name(op.type, 'mul')
+        node2 = helper.make_node(
+            'Mul', inputs=[op.input('X')[0], name1], outputs=[name2])
+        node3 = helper.make_node(
+            'Div', inputs=[name2, scale_name], outputs=op.output('Out'))
+        return [
+            min_node, max_node, scale_node, offset_node, node0, node1, node2,
+            node3
+        ]
+
+    def elementwise_mul(self, op, block):
+        axis = op.attr('axis')
+        x_shape = block.var(op.input('X')[0]).shape
+        y_shape = block.var(op.input('Y')[0]).shape
+        if len(y_shape) == 1 and axis == 1:
+            shape_name = self.get_name(op.type, 'shape')
+            shape_value = [1] * len(x_shape)
+            shape_value[axis] = y_shape[0]
+            shape_node = self.make_constant_node(
+                shape_name, onnx_pb.TensorProto.INT64, shape_value)
+            temp_value = self.get_name(op.type, 'temp')
+            y_node = helper.make_node(
+                'Reshape',
+                inputs=[op.input('Y')[0], shape_name],
+                outputs=[temp_value])
+            node = helper.make_node(
+                'Mul',
+                inputs=[op.input('X')[0], temp_value],
+                outputs=op.output('Out'))
+            return [shape_node, y_node, node]
+        elif len(x_shape) == len(y_shape):
+            node = helper.make_node(
+                'Mul',
+                inputs=[op.input('X')[0], op.input('Y')[0]],
+                outputs=op.output('Out'))
+            return node
+        else:
+            raise Excpetion("Unexpected situation happend in elementwise_add")
+        return node
+
+    def feed(self, op, block):
+        name = op.output('Out')[0]
+        var = block.var(name)
+        tensor_info = helper.make_tensor_value_info(
+            name=name,
+            shape=var.shape,
+            elem_type=self.paddle_onnx_dtype_map[var.dtype])
+        return tensor_info
+
+    def fetch(self, op, block):
+        name = op.input('X')[0]
+        var = block.var(name)
+        tensor_info = helper.make_tensor_value_info(
+            name=name,
+            shape=var.shape,
+            elem_type=self.paddle_onnx_dtype_map[var.dtype])
+        return tensor_info
+
+    def unsqueeze2(self, op, block):
+        node = helper.make_node(
+            'Unsqueeze',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            axes=op.attr('axes'))
+        return node
+
+    def arg_max(self, op, block):
+        node = helper.make_node(
+            'ArgMax',
+            inputs=op.input('X'),
+            outputs=op.output('Out'),
+            axis=op.attr('axis'),
+            keepdims=0)
+        return node
+
+    def reciprocal(self, op, block):
+        inputs = op.input(op.input_names[0])
+        outputs = op.output(op.output_names[0])
+        node = helper.make_node('Reciprocal', inputs=inputs, outputs=outputs)
+        return node
+
+    def im2sequence(self, op, block):
+        from .paddle_custom_layer.im2sequence import im2sequence
+        return im2sequence(op, block)

x2paddle/op_mapper/tf_op_mapper.py

@@ -85,7 +85,8 @@ class TFOpMapper(OpMapper):
 
         not_placeholder = list()
         for name in self.graph.input_nodes:
-            if self.graph.get_node(name).layer_type != "Placeholder" and self.graph.get_node(name).layer_type != "OneShotIterator":
+            if self.graph.get_node(name).layer_type != "Placeholder" \
+               and self.graph.get_node(name).layer_type != "OneShotIterator":
                 not_placeholder.append(name)
         for name in not_placeholder:
             idx = self.graph.input_nodes.index(name)
@@ -113,9 +114,8 @@ class TFOpMapper(OpMapper):
             else:
                 unsupported_ops.add(op)
         if len(unsupported_ops) > 0:
-            sys.stderr.write(
-                "=========={} Ops are not supported yet======\n".format(
-                    len(unsupported_ops)))
+            sys.stderr.write("=========={} Ops are not supported yet======\n".
+                             format(len(unsupported_ops)))
             for op in unsupported_ops:
                 sys.stderr.write("========== {} ==========\n".format(op))
             sys.exit(-1)
@@ -140,10 +140,8 @@ class TFOpMapper(OpMapper):
             pd_param_name = list(param.values())[0]
             tf_param = node.get_attr(tf_param_name)
             attr[pd_param_name] = tf_param
-        node.fluid_code.add_layer(op_info[0],
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            op_info[0], inputs=input, output=node, param_attr=attr)
 
     def elementwise_map(self, node):
         assert node.layer_type in self.elementwise_ops
@@ -178,21 +176,21 @@ class TFOpMapper(OpMapper):
                         0] == y_shape[-1] and y_shape.count(-1) < 1:
                     shape = [1, x_shape[0], 1, 1]
                     attr = {"shape": shape}
-                    node.fluid_code.add_layer("reshape",
+                    node.fluid_code.add_layer(
+                        "reshape",
                         inputs=x_input,
                         output="reshape_x",
                         param_attr=attr)
                     if y_shape[0] != 1:
                         attr = {"expand_times": [y_shape[0], 1, 1, 1]}
-                        node.fluid_code.add_layer("expand",
+                        node.fluid_code.add_layer(
+                            "expand",
                             inputs="reshape_x",
                             output="reshape_x",
                             param_attr=attr)
                     inputs = {"x": "reshape_x", "y": y_input}
-                    node.fluid_code.add_layer(op_type,
-                                              inputs=inputs,
-                                              output=node,
-                                              param_attr=None)
+                    node.fluid_code.add_layer(
+                        op_type, inputs=inputs, output=node, param_attr=None)
                     return
                 else:
                     raise Exception("Unexpected situation happend")
@@ -204,10 +202,8 @@ class TFOpMapper(OpMapper):
                 axis = -1
             attr = {"axis": axis}
             inputs = {"x": x_input, "y": y_input}
-            node.fluid_code.add_layer(op_type,
-                                      inputs=inputs,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                op_type, inputs=inputs, output=node, param_attr=attr)
             return
 
         is_sub_seq = True
@@ -241,10 +237,8 @@ class TFOpMapper(OpMapper):
                 if len(x_expand_times) == 4 and x.tf_data_format == "NHWC":
                     x_expand_times = [x_expand_times[i] for i in [0, 3, 1, 2]]
                 attr = {"expand_times": x_expand_times}
-                node.fluid_code.add_layer("expand",
-                                          inputs=x_input,
-                                          output="x_tmp",
-                                          param_attr=attr)
+                node.fluid_code.add_layer(
+                    "expand", inputs=x_input, output="x_tmp", param_attr=attr)
                 x_input = "x_tmp"
             if y_need_expand:
                 if len(y_expand_times) == 3 and y.tf_data_format == "NHWC":
@@ -252,16 +246,12 @@ class TFOpMapper(OpMapper):
                 if len(y_expand_times) == 4 and y.tf_data_format == "NHWC":
                     y_expand_times = [y_expand_times[i] for i in [0, 3, 1, 2]]
                 attr = {"expand_times": y_expand_times}
-                node.fluid_code.add_layer("expand",
-                                          inputs=y_input,
-                                          output="y_tmp",
-                                          param_attr=attr)
+                node.fluid_code.add_layer(
+                    "expand", inputs=y_input, output="y_tmp", param_attr=attr)
                 y_input = "y_tmp"
         inputs = {"x": x_input, "y": y_input}
-        node.fluid_code.add_layer(op_type,
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=None)
+        node.fluid_code.add_layer(
+            op_type, inputs=inputs, output=node, param_attr=None)
 
     def Placeholder(self, node):
         shape = node.out_shapes[0]
@@ -282,10 +272,8 @@ class TFOpMapper(OpMapper):
         if shape[0] < 0:
             self.batch_node = node
 
-        node.fluid_code.add_layer("data",
-                                  inputs=None,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "data", inputs=None, output=node, param_attr=attr)
 
     def OneShotIterator(self, node):
         return self.Placeholder(node)
@@ -307,8 +295,8 @@ class TFOpMapper(OpMapper):
                 shape = [shape[i] for i in [0, 3, 1, 2]]
             if len(shape) == 3:
                 shape = [shape[i] for i in [2, 0, 1]]
-                self.weights[node.layer_name] = numpy.transpose(
-                    node.value, (2, 0, 1))
+                self.weights[node.layer_name] = numpy.transpose(node.value,
+                                                                (2, 0, 1))
         elif node.tf_data_format == "NCHW":
             if len(shape) == 4:
                 self.graph.data_format_propagation(node)
@@ -319,10 +307,8 @@ class TFOpMapper(OpMapper):
             'name': string(node.layer_name),
             'default_initializer': initializer
         }
-        node.fluid_code.add_layer("create_parameter",
-                                  inputs=None,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "create_parameter", inputs=None, output=node, param_attr=attr)
 
     def Transpose(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -361,16 +347,12 @@ class TFOpMapper(OpMapper):
             node.tf_data_format = [tf_data_format[i] for i in perm]
             node.pd_data_format = [pd_data_format[i] for i in perm]
             attr = {'perm': new_perm}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=input, output=node, param_attr=attr)
         elif len(node.out_shapes[0]) != 4:
             attr = {'perm': perm}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=input, output=node, param_attr=attr)
         else:
             raise Exception("Unexpected situation happend in Transpose OP")
 
@@ -400,10 +382,8 @@ class TFOpMapper(OpMapper):
             "pool_padding": string(pad_mode),
             "pool_stride": strides[2:4]
         }
-        node.fluid_code.add_layer("pool2d",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "pool2d", inputs=input, output=node, param_attr=attr)
 
     def Conv2D(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -443,10 +423,8 @@ class TFOpMapper(OpMapper):
             "dilation": dilations[2:4],
             "padding": string(pad_mode)
         }
-        node.fluid_code.add_layer("conv2d",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "conv2d", inputs=input, output=node, param_attr=attr)
 
     def BiasAdd(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -456,10 +434,8 @@ class TFOpMapper(OpMapper):
             axis = 1
         inputs = {"x": input, "y": bias}
         attr = {"axis": axis}
-        node.fluid_code.add_layer("elementwise_add",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "elementwise_add", inputs=inputs, output=node, param_attr=attr)
 
     def FusedBatchNorm(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -490,10 +466,8 @@ class TFOpMapper(OpMapper):
             "is_test": True
         }
 
-        node.fluid_code.add_layer("batch_norm",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "batch_norm", inputs=input, output=node, param_attr=attr)
 
     def FusedBatchNormV3(self, node):
         return self.FusedBatchNorm(node)
@@ -538,10 +512,8 @@ class TFOpMapper(OpMapper):
             "use_cudnn": False,
             "padding": string(pad_mode)
         }
-        node.fluid_code.add_layer("conv2d",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "conv2d", inputs=input, output=node, param_attr=attr)
 
     def Reshape(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -561,18 +533,17 @@ class TFOpMapper(OpMapper):
                 attr = {"shape": shape}
                 self.add_omit_nodes(param.layer_name, node.layer_name)
             else:
-                assert len(param.out_shapes[0]
-                           ) == 1, "Unexpected situation of shape parameter"
+                assert len(param.out_shapes[
+                    0]) == 1, "Unexpected situation of shape parameter"
                 attr = {"shape": [-1]}
-                node.fluid_code.add_layer("reshape",
+                node.fluid_code.add_layer(
+                    "reshape",
                     inputs=param,
                     output="shape_param",
                     param_attr=attr)
                 attr = {"num_or_sections": param.out_shapes[0][0], "dim": 0}
-                node.fluid_code.add_layer("split",
-                                          inputs="shape_param",
-                                          output=node,
-                                          param_attr=attr)
+                node.fluid_code.add_layer(
+                    "split", inputs="shape_param", output=node, param_attr=attr)
                 new_param = "["
                 for i in range(param.out_shapes[0][0]):
                     new_param += (node.layer_name + "[{}]".format(i) + ", ")
@@ -600,14 +571,10 @@ class TFOpMapper(OpMapper):
         if len(input.out_shapes[0]) == 4 and node.tf_data_format == "NHWC":
             if len(attr["shape"]) < 3:
                 perm = {"perm": [0, 2, 3, 1]}
-                node.fluid_code.add_layer("transpose",
-                                          inputs=input,
-                                          output=node,
-                                          param_attr=perm)
-                node.fluid_code.add_layer("reshape",
-                                          inputs=node,
-                                          output=node,
-                                          param_attr=attr)
+                node.fluid_code.add_layer(
+                    "transpose", inputs=input, output=node, param_attr=perm)
+                node.fluid_code.add_layer(
+                    "reshape", inputs=node, output=node, param_attr=attr)
                 return
 
         if len(attr["shape"]) == 4 and node.tf_data_format == "NHWC":
@@ -616,27 +583,19 @@ class TFOpMapper(OpMapper):
                 attr["shape"] = [attr["shape"][i] for i in [0, 3, 1, 2]]
             else:
                 perm = {"perm": [0, 2, 3, 1]}
-                node.fluid_code.add_layer("transpose",
-                                          inputs=input,
-                                          output=node,
-                                          param_attr=perm)
-                node.fluid_code.add_layer("reshape",
-                                          inputs=node,
-                                          output=node,
-                                          param_attr=attr)
+                node.fluid_code.add_layer(
+                    "transpose", inputs=input, output=node, param_attr=perm)
+                node.fluid_code.add_layer(
+                    "reshape", inputs=node, output=node, param_attr=attr)
                 perm = {"perm": [0, 3, 1, 2]}
-                node.fluid_code.add_layer("transpose",
-                                          inputs=node,
-                                          output=node,
-                                          param_attr=perm)
+                node.fluid_code.add_layer(
+                    "transpose", inputs=node, output=node, param_attr=perm)
                 return
         if len(attr["shape"]) == 5:
             attr["shape"] = [attr["shape"][i] for i in [0, 1, 4, 2, 3]]
 
-        node.fluid_code.add_layer("reshape",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "reshape", inputs=input, output=node, param_attr=attr)
 
     def AvgPool(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -664,10 +623,8 @@ class TFOpMapper(OpMapper):
             "pool_stride": strides[2:4],
             "pool_padding": string(pad_mode)
         }
-        node.fluid_code.add_layer("pool2d",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "pool2d", inputs=input, output=node, param_attr=attr)
 
     def SplitV(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -684,28 +641,24 @@ class TFOpMapper(OpMapper):
             "num_or_sections": num_sections.value.tolist(),
             "dim": dim.value
         }
-        node.fluid_code.add_layer("split",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "split", inputs=input, output=node, param_attr=attr)
 
     def ConcatV2(self, node):
         inputs = [
-            self.graph.get_node(name, copy=True)
-            for name in node.layer.input[:-1]
+            self.graph.get_node(
+                name, copy=True) for name in node.layer.input[:-1]
         ]
         axis = self.graph.get_node(node.layer.input[-1], copy=True)
         assert axis.layer_type == "Const"
         self.add_omit_nodes(axis.layer_name, node.layer_name)
         axis = axis.value
-        if inputs[0].tf_data_format == "NHWC" and len(
-                inputs[0].out_shapes[0]) == 4:
+        if inputs[0].tf_data_format == "NHWC" and len(inputs[0].out_shapes[
+                0]) == 4:
             axis = nhwc_dim_to_nchw(inputs[0], axis)
         attr = {"axis": axis}
-        node.fluid_code.add_layer("concat",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "concat", inputs=inputs, output=node, param_attr=attr)
 
     def Tile(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -725,18 +678,17 @@ class TFOpMapper(OpMapper):
                 expand_times[i] = 1
 
         attr = {"expand_times": expand_times}
-        node.fluid_code.add_layer("expand",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "expand", inputs=input, output=node, param_attr=attr)
 
     def Pack(self, node):
         inputs = [
-            self.graph.get_node(name, copy=True) for name in node.layer.input
+            self.graph.get_node(
+                name, copy=True) for name in node.layer.input
         ]
         axis = node.get_attr("axis")
-        if inputs[0].tf_data_format == "NHWC" and len(
-                inputs[0].out_shapes[0]) == 4:
+        if inputs[0].tf_data_format == "NHWC" and len(inputs[0].out_shapes[
+                0]) == 4:
             tf_data_format = list(inputs[0].tf_data_format)
             tf_data_format.insert(axis, str(len(tf_data_format)))
             axis = nhwc_dim_to_nchw(inputs[0], axis)
@@ -746,10 +698,8 @@ class TFOpMapper(OpMapper):
             node.pd_data_format = "".join(pd_data_format)
 
         attr = {"axis": axis}
-        node.fluid_code.add_layer("stack",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "stack", inputs=inputs, output=node, param_attr=attr)
 
     def Pad(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -766,10 +716,8 @@ class TFOpMapper(OpMapper):
                 paddings = paddings[4:]
                 pad_op = "pad2d"
         attr = {"paddings": paddings}
-        node.fluid_code.add_layer(pad_op,
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            pad_op, inputs=input, output=node, param_attr=attr)
 
     def MirrorPad(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -788,10 +736,8 @@ class TFOpMapper(OpMapper):
                 paddings = paddings[4:]
                 pad_op = "pad2d"
         attr = {"paddings": paddings, "mode": string("reflect")}
-        node.fluid_code.add_layer(pad_op,
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            pad_op, inputs=input, output=node, param_attr=attr)
 
     def Range(self, node):
         start = self.graph.get_node(node.layer.input[0], copy=True)
@@ -815,10 +761,8 @@ class TFOpMapper(OpMapper):
 
         inputs = {"start": start, "end": limit, "step": delta}
         attr = {"dtype": string(node.dtype)}
-        node.fluid_code.add_layer("range",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "range", inputs=inputs, output=node, param_attr=attr)
 
     def Mean(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -832,10 +776,8 @@ class TFOpMapper(OpMapper):
                 dims[i] = nhwc_dim_to_nchw(input, dims[i])
 
         attr = {"dim": dims, "keep_dim": keep_dims}
-        node.fluid_code.add_layer("reduce_mean",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "reduce_mean", inputs=input, output=node, param_attr=attr)
 
     def MatMul(self, node):
         x = self.graph.get_node(node.layer.input[0], copy=True)
@@ -849,15 +791,11 @@ class TFOpMapper(OpMapper):
             shape = x.out_shapes[0]
             shape[-1] = y.out_shapes[0][0]
             attr = {"shape": shape}
-            node.fluid_code.add_layer("reshape",
-                                      inputs=x,
-                                      output=x,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "reshape", inputs=x, output=x, param_attr=attr)
         attr = {"transpose_x": transpose_a, "transpose_y": transpose_b}
-        node.fluid_code.add_layer("matmul",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "matmul", inputs=inputs, output=node, param_attr=attr)
 
     def ArgMax(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -868,10 +806,8 @@ class TFOpMapper(OpMapper):
         if input.tf_data_format == "NHWC" and len(input.out_shapes[0]) == 4:
             axis = nhwc_dim_to_nchw(input, axis)
         attr = {"axis": axis}
-        node.fluid_code.add_layer("argmax",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "argmax", inputs=input, output=node, param_attr=attr)
 
     def StridedSlice(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -909,16 +845,12 @@ class TFOpMapper(OpMapper):
             x = shrink_axis_mask >> i & 1
             if x == 1:
                 squeeze_dims.append(i)
-        node.fluid_code.add_layer("slice",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "slice", inputs=input, output=node, param_attr=attr)
         if shrink_axis_mask > 0 and len(input.out_shapes[0]) == 5:
             attr = {"axes": squeeze_dims}
-            node.fluid_code.add_layer("squeeze",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "squeeze", inputs=node, output=node, param_attr=attr)
 
     def Slice(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -950,10 +882,8 @@ class TFOpMapper(OpMapper):
             "starts": begin,
             "ends": size
         }
-        node.fluid_code.add_layer("slice",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "slice", inputs=input, output=node, param_attr=attr)
 
     def Conv2DBackpropInput(self, node):
         out_shape = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1003,10 +933,8 @@ class TFOpMapper(OpMapper):
             "padding": string(pad_mode),
             "output_size": out_shape[1:3]
         }
-        node.fluid_code.add_layer("conv2d_transpose",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "conv2d_transpose", inputs=input, output=node, param_attr=attr)
 
     def Max(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1018,10 +946,8 @@ class TFOpMapper(OpMapper):
             dim = nhwc_dim_to_nchw(input, dim)
 
         attr = {"dim": dim, "keep_dim": keep_dims}
-        node.fluid_code.add_layer("reduce_max",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "reduce_max", inputs=input, output=node, param_attr=attr)
 
     def Sum(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1033,19 +959,15 @@ class TFOpMapper(OpMapper):
             dim = nhwc_dim_to_nchw(input, dim)
 
         attr = {"dim": dim, "keep_dim": keep_dims}
-        node.fluid_code.add_layer("reduce_sum",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "reduce_sum", inputs=input, output=node, param_attr=attr)
 
     def Cast(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         dtype = node.dtype_map[node.get_attr('DstT')]
         attr = {"dtype": string(dtype)}
-        node.fluid_code.add_layer("cast",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "cast", inputs=input, output=node, param_attr=attr)
 
     def Split(self, node):
         dim = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1057,10 +979,8 @@ class TFOpMapper(OpMapper):
             dim = nhwc_dim_to_nchw(input, dim)
 
         attr = {"num_or_sections": num_split, "dim": dim}
-        node.fluid_code.add_layer("split",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "split", inputs=input, output=node, param_attr=attr)
 
     def Squeeze(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1069,10 +989,8 @@ class TFOpMapper(OpMapper):
             for i in range(len(squeeze_dims)):
                 squeeze_dims[i] = nhwc_dim_to_nchw(input, squeeze_dims[i])
         attr = {"axes": squeeze_dims}
-        node.fluid_code.add_layer("squeeze",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "squeeze", inputs=input, output=node, param_attr=attr)
 
     def Softmax(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1082,10 +1000,8 @@ class TFOpMapper(OpMapper):
         if input.tf_data_format == "NHWC" and len(input.out_shapes[0]) == 4:
             axis = nhwc_dim_to_nchw(input, axis)
         attr = {"axis": axis}
-        node.fluid_code.add_layer("softmax",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "softmax", inputs=input, output=node, param_attr=attr)
 
     def ResizeNearestNeighbor(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1094,14 +1010,12 @@ class TFOpMapper(OpMapper):
         if resize_shape.layer_type == "Const":
             resize_shape = resize_shape.value.tolist()
         else:
-            resize_shape = self.decoder.infer_shape_tensor(
-                resize_shape, node.out_shapes[0])
+            resize_shape = self.decoder.infer_shape_tensor(resize_shape,
+                                                           node.out_shapes[0])
         align_corners = node.get_attr("align_corners")
         attr = {"align_corners": align_corners, "out_shape": resize_shape}
-        node.fluid_code.add_layer("resize_nearest",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "resize_nearest", inputs=input, output=node, param_attr=attr)
 
     def ResizeBilinear(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1110,27 +1024,23 @@ class TFOpMapper(OpMapper):
         if resize_shape.layer_type == "Const":
             resize_shape = resize_shape.value.tolist()
         else:
-            resize_shape = self.decoder.infer_shape_tensor(
-                resize_shape, node.out_shapes[0])
+            resize_shape = self.decoder.infer_shape_tensor(resize_shape,
+                                                           node.out_shapes[0])
         align_corners = node.get_attr("align_corners")
         attr = {
             "align_corners": align_corners,
             "out_shape": resize_shape,
             "align_mode": 1
         }
-        node.fluid_code.add_layer("resize_bilinear",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "resize_bilinear", inputs=input, output=node, param_attr=attr)
 
     def GreaterEqual(self, node):
         x = self.graph.get_node(node.layer.input[0], copy=True)
         y = self.graph.get_node(node.layer.input[1], copy=True)
         inputs = {"x": x, "y": y}
-        node.fluid_code.add_layer("greater_equal",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=None)
+        node.fluid_code.add_layer(
+            "greater_equal", inputs=inputs, output=node, param_attr=None)
 
     def RandomUniform(self, node):
         shape = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1144,26 +1054,21 @@ class TFOpMapper(OpMapper):
         attr = {"shape": shape, "min": 0.0, "max": 0.9999}
         if shape[0] < 0:
             input = self.batch_node
-            node.fluid_code.add_layer("uniform_random_batch_size_like",
+            node.fluid_code.add_layer(
+                "uniform_random_batch_size_like",
                 inputs=input,
                 output=node,
                 param_attr=attr)
         else:
-            node.fluid_code.add_layer("uniform_random",
-                                      inputs=None,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "uniform_random", inputs=None, output=node, param_attr=attr)
 
     def SquaredDifference(self, node):
         x = self.graph.get_node(node.layer.input[0], copy=True)
         y = self.graph.get_node(node.layer.input[1], copy=True)
         inputs = {"x": x, "y": y}
-        node.fluid_code.add_layer("elementwise_sub",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=None)
+        node.fluid_code.add_layer(
+            "elementwise_sub", inputs=inputs, output=node, param_attr=None)
         inputs = {"x": node, "y": node}
-        node.fluid_code.add_layer("elementwise_mul",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=None)
+        node.fluid_code.add_layer(
+            "elementwise_mul", inputs=inputs, output=node, param_attr=None)

x2paddle/op_mapper/tf_op_mapper_nhwc.py

@@ -43,6 +43,7 @@ class TFOpMapperNHWC(OpMapper):
         'Sqrt': ['sqrt'],
         'swish_f32': ['swish'],
         'Tanh': ['tanh'],
+        'Softplus': ['softplus'],
         'LeakyRelu': ['leaky_relu', {
             'alpha': 'alpha'
         }]
@@ -128,26 +129,18 @@ class TFOpMapperNHWC(OpMapper):
 
         if len(input.out_shapes[0]) == 4 and op_info[0] != 'shape':
             attr1 = {"perm": [0, 3, 1, 2]}
-            node.fluid_code.add_layer('transpose',
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr1)
+            node.fluid_code.add_layer(
+                'transpose', inputs=input, output=node, param_attr=attr1)
             input = node
-            node.fluid_code.add_layer(op_info[0],
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                op_info[0], inputs=input, output=node, param_attr=attr)
             input = node
             attr2 = {"perm": [0, 2, 3, 1]}
-            node.fluid_code.add_layer('transpose',
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr2)
+            node.fluid_code.add_layer(
+                'transpose', inputs=input, output=node, param_attr=attr2)
         else:
-            node.fluid_code.add_layer(op_info[0],
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                op_info[0], inputs=input, output=node, param_attr=attr)
 
     def elementwise_map(self, node):
         assert node.layer_type in self.elementwise_ops
@@ -208,42 +201,37 @@ class TFOpMapperNHWC(OpMapper):
                         raise Exception("Unexpected situation happend")
             if x_need_expand:
                 attr = {"expand_times": x_expand_times}
-                node.fluid_code.add_layer("expand",
-                                          inputs=x_input,
-                                          output="x_tmp",
-                                          param_attr=attr)
+                node.fluid_code.add_layer(
+                    "expand", inputs=x_input, output="x_tmp", param_attr=attr)
                 x_input = "x_tmp"
             if y_need_expand:
                 attr = {"expand_times": y_expand_times}
-                node.fluid_code.add_layer("expand",
-                                          inputs=y_input,
-                                          output="y_tmp",
-                                          param_attr=attr)
+                node.fluid_code.add_layer(
+                    "expand", inputs=y_input, output="y_tmp", param_attr=attr)
                 y_input = "y_tmp"
         if len(x_shape) == 4 and len(y_shape) == 4:
-            node.fluid_code.add_layer("transpose",
+            node.fluid_code.add_layer(
+                "transpose",
                 inputs=x_input,
                 output=x_input,
                 param_attr={'perm': [0, 3, 1, 2]})
-            node.fluid_code.add_layer("transpose",
+            node.fluid_code.add_layer(
+                "transpose",
                 inputs=y_input,
                 output=y_input,
                 param_attr={'perm': [0, 3, 1, 2]})
             inputs = {"x": x_input, "y": y_input}
-            node.fluid_code.add_layer(op_type,
-                                      inputs=inputs,
-                                      output=node,
-                                      param_attr=None)
-            node.fluid_code.add_layer("transpose",
+            node.fluid_code.add_layer(
+                op_type, inputs=inputs, output=node, param_attr=None)
+            node.fluid_code.add_layer(
+                "transpose",
                 inputs=node,
                 output=node,
                 param_attr={'perm': [0, 2, 3, 1]})
         else:
             inputs = {"x": x_input, "y": y_input}
-            node.fluid_code.add_layer(op_type,
-                                      inputs=inputs,
-                                      output=node,
-                                      param_attr=None)
+            node.fluid_code.add_layer(
+                op_type, inputs=inputs, output=node, param_attr=None)
 
     def Placeholder(self, node):
         shape = node.out_shapes[0]
@@ -259,10 +247,8 @@ class TFOpMapperNHWC(OpMapper):
             'append_batch_size': False
         }
 
-        node.fluid_code.add_layer("data",
-                                  inputs=None,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "data", inputs=None, output=node, param_attr=attr)
 
     def Const(self, node):
         shape = node.out_shapes[0]
@@ -282,10 +268,8 @@ class TFOpMapperNHWC(OpMapper):
             'name': string(node.layer_name),
             'default_initializer': initializer
         }
-        node.fluid_code.add_layer("create_parameter",
-                                  inputs=None,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "create_parameter", inputs=None, output=node, param_attr=attr)
 
     def Transpose(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -296,10 +280,8 @@ class TFOpMapperNHWC(OpMapper):
         perm = perm.value.tolist()
 
         attr = {'perm': perm}
-        node.fluid_code.add_layer("transpose",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "transpose", inputs=input, output=node, param_attr=attr)
 
     def MaxPool(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -316,10 +298,8 @@ class TFOpMapperNHWC(OpMapper):
 
         if not channel_first:
             attr = {"perm": [0, 3, 1, 2]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=input, output=node, param_attr=attr)
             in_shape = [in_shape[i] for i in [0, 3, 1, 2]]
             strides = [strides[i] for i in [0, 3, 1, 2]]
             k_size = [k_size[i] for i in [0, 3, 1, 2]]
@@ -331,17 +311,13 @@ class TFOpMapperNHWC(OpMapper):
             "pool_stride": strides[2:4],
             "pool_padding": string(pad_mode)
         }
-        node.fluid_code.add_layer("pool2d",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "pool2d", inputs=input, output=node, param_attr=attr)
 
         if not channel_first:
             attr = {"perm": [0, 2, 3, 1]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=node, output=node, param_attr=attr)
 
     def Conv2D(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -373,10 +349,8 @@ class TFOpMapperNHWC(OpMapper):
             strides = [strides[i] for i in [0, 3, 1, 2]]
             dilations = [dilations[i] for i in [0, 3, 1, 2]]
             attr = {"perm": [0, 3, 1, 2]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=input, output=node, param_attr=attr)
             input = node
 
         attr = {
@@ -393,25 +367,19 @@ class TFOpMapperNHWC(OpMapper):
             if len(node.dilation) == 1:
                 attr['dilation'] = [1, node.dilation[0]]
 
-        node.fluid_code.add_layer("conv2d",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "conv2d", inputs=input, output=node, param_attr=attr)
         if not channel_first:
             attr = {"perm": [0, 2, 3, 1]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=node, output=node, param_attr=attr)
 
     def BiasAdd(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         bias = self.graph.get_node(node.layer.input[1], copy=True)
         inputs = {"x": input, "y": bias}
-        node.fluid_code.add_layer("elementwise_add",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=None)
+        node.fluid_code.add_layer(
+            "elementwise_add", inputs=inputs, output=node, param_attr=None)
 
     def FusedBatchNorm(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -433,10 +401,8 @@ class TFOpMapperNHWC(OpMapper):
 
         if not channel_first:
             attr = {"perm": [0, 3, 1, 2]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=input, output=node, param_attr=attr)
             input = node
 
         attr = {
@@ -448,17 +414,13 @@ class TFOpMapperNHWC(OpMapper):
             "is_test": True
         }
 
-        node.fluid_code.add_layer("batch_norm",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "batch_norm", inputs=input, output=node, param_attr=attr)
 
         if not channel_first:
             attr = {"perm": [0, 2, 3, 1]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=node, output=node, param_attr=attr)
 
     def DepthwiseConv2dNative(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -487,10 +449,8 @@ class TFOpMapperNHWC(OpMapper):
             strides = [strides[i] for i in [0, 3, 1, 2]]
             dilations = [dilations[i] for i in [0, 3, 1, 2]]
             attr = {"perm": [0, 3, 1, 2]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=input, output=node, param_attr=attr)
             input = node
 
         attr = {
@@ -504,17 +464,13 @@ class TFOpMapperNHWC(OpMapper):
             "use_cudnn": False,
             "padding": string(pad_mode)
         }
-        node.fluid_code.add_layer("conv2d",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "conv2d", inputs=input, output=node, param_attr=attr)
 
         if not channel_first:
             attr = {"perm": [0, 2, 3, 1]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=node, output=node, param_attr=attr)
 
     def Reshape(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -530,18 +486,17 @@ class TFOpMapperNHWC(OpMapper):
                 attr = {"shape": shape}
                 self.add_omit_nodes(param.layer_name, node.layer_name)
             else:
-                assert len(param.out_shapes[0]
-                           ) == 1, "Unexpected situation of shape parameter"
+                assert len(param.out_shapes[
+                    0]) == 1, "Unexpected situation of shape parameter"
                 attr = {"shape": [-1]}
-                node.fluid_code.add_layer("reshape",
+                node.fluid_code.add_layer(
+                    "reshape",
                     inputs=param,
                     output="shape_param",
                     param_attr=attr)
                 attr = {"num_or_sections": param.out_shapes[0][0], "dim": 0}
-                node.fluid_code.add_layer("split",
-                                          inputs="shape_param",
-                                          output=node,
-                                          param_attr=attr)
+                node.fluid_code.add_layer(
+                    "split", inputs="shape_param", output=node, param_attr=attr)
                 new_param = "["
                 for i in range(param.out_shapes[0][0]):
                     new_param += (node.layer_name + "[{}]".format(i) + ", ")
@@ -565,10 +520,8 @@ class TFOpMapperNHWC(OpMapper):
                 attr["shape"][index] = int(total_size)
                 attr["shape"][0] = -1
 
-        node.fluid_code.add_layer("reshape",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "reshape", inputs=input, output=node, param_attr=attr)
 
     def AvgPool(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -588,10 +541,8 @@ class TFOpMapperNHWC(OpMapper):
             strides = [strides[i] for i in [0, 3, 1, 2]]
             k_size = [k_size[i] for i in [0, 3, 1, 2]]
             attr = {"perm": [0, 3, 1, 2]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=input, output=node, param_attr=attr)
             input = node
 
         attr = {
@@ -600,17 +551,13 @@ class TFOpMapperNHWC(OpMapper):
             "pool_stride": strides[2:4],
             "pool_padding": string(pad_mode)
         }
-        node.fluid_code.add_layer("pool2d",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "pool2d", inputs=input, output=node, param_attr=attr)
 
         if not channel_first:
             attr = {"perm": [0, 2, 3, 1]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=node, output=node, param_attr=attr)
 
     def SplitV(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -625,15 +572,13 @@ class TFOpMapperNHWC(OpMapper):
             "num_or_sections": num_sections.value.tolist(),
             "dim": dim.value
         }
-        node.fluid_code.add_layer("split",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "split", inputs=input, output=node, param_attr=attr)
 
     def ConcatV2(self, node):
         inputs = [
-            self.graph.get_node(name, copy=True)
-            for name in node.layer.input[:-1]
+            self.graph.get_node(
+                name, copy=True) for name in node.layer.input[:-1]
         ]
         axis = self.graph.get_node(node.layer.input[-1], copy=True)
         assert axis.layer_type == "Const"
@@ -643,10 +588,8 @@ class TFOpMapperNHWC(OpMapper):
             axis += len(inputs[0].out_shapes[0])
 
         attr = {"axis": axis}
-        node.fluid_code.add_layer("concat",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "concat", inputs=inputs, output=node, param_attr=attr)
 
     def Tile(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -660,21 +603,18 @@ class TFOpMapperNHWC(OpMapper):
             if expand_times[i] < 0:
                 expand_times[i] = 1
         attr = {"expand_times": expand_times}
-        node.fluid_code.add_layer("expand",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "expand", inputs=input, output=node, param_attr=attr)
 
     def Pack(self, node):
         inputs = [
-            self.graph.get_node(name, copy=True) for name in node.layer.input
+            self.graph.get_node(
+                name, copy=True) for name in node.layer.input
         ]
         axis = node.get_attr("axis")
         attr = {"axis": axis}
-        node.fluid_code.add_layer("stack",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "stack", inputs=inputs, output=node, param_attr=attr)
 
     def Pad(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -695,30 +635,22 @@ class TFOpMapperNHWC(OpMapper):
             if new_padding is not None:
                 if input.tf_data_format == "NHWC":
                     attr = {"perm": [0, 3, 1, 2]}
-                    node.fluid_code.add_layer("transpose",
-                                              inputs=input,
-                                              output=node,
-                                              param_attr=attr)
+                    node.fluid_code.add_layer(
+                        "transpose", inputs=input, output=node, param_attr=attr)
                     input = node
                 attr = {"paddings": new_padding}
-                node.fluid_code.add_layer("pad2d",
-                                          inputs=input,
-                                          output=node,
-                                          param_attr=attr)
+                node.fluid_code.add_layer(
+                    "pad2d", inputs=input, output=node, param_attr=attr)
                 if input.tf_data_format == "NHWC":
                     attr = {"perm": [0, 2, 3, 1]}
-                    node.fluid_code.add_layer("transpose",
-                                              inputs=node,
-                                              output=node,
-                                              param_attr=attr)
+                    node.fluid_code.add_layer(
+                        "transpose", inputs=node, output=node, param_attr=attr)
 
                 return
 
         attr = {"paddings": paddings}
-        node.fluid_code.add_layer("pad",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "pad", inputs=input, output=node, param_attr=attr)
 
     def Range(self, node):
         start = self.graph.get_node(node.layer.input[0], copy=True)
@@ -746,10 +678,8 @@ class TFOpMapperNHWC(OpMapper):
             "step": delta,
         }
         attr = {"dtype": string(node.dtype)}
-        node.fluid_code.add_layer("range",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "range", inputs=inputs, output=node, param_attr=attr)
 
     def Mean(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -759,10 +689,8 @@ class TFOpMapperNHWC(OpMapper):
         keep_dims = node.get_attr("keep_dims")
 
         attr = {"dim": dims, "keep_dim": keep_dims}
-        node.fluid_code.add_layer("reduce_mean",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "reduce_mean", inputs=input, output=node, param_attr=attr)
 
     def MatMul(self, node):
         x = self.graph.get_node(node.layer.input[0], copy=True)
@@ -776,15 +704,11 @@ class TFOpMapperNHWC(OpMapper):
             shape = x.out_shapes[0]
             shape[-1] = y.out_shapes[0][0]
             attr = {"shape": shape}
-            node.fluid_code.add_layer("reshape",
-                                      inputs=x,
-                                      output=x,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "reshape", inputs=x, output=x, param_attr=attr)
         attr = {"transpose_x": transpose_a, "transpose_y": transpose_b}
-        node.fluid_code.add_layer("matmul",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "matmul", inputs=inputs, output=node, param_attr=attr)
 
     def ArgMax(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -793,10 +717,8 @@ class TFOpMapperNHWC(OpMapper):
         self.add_omit_nodes(axis.layer_name, node.layer_name)
         axis = axis.value
         attr = {"axis": axis}
-        node.fluid_code.add_layer("argmax",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "argmax", inputs=input, output=node, param_attr=attr)
 
     def StridedSlice(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -862,25 +784,19 @@ class TFOpMapperNHWC(OpMapper):
             "starts": new_begin,
             "ends": new_end
         }
-        node.fluid_code.add_layer("slice",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "slice", inputs=input, output=node, param_attr=attr)
         if len(new_axes) > 0:
             attr = {"axes": new_axes}
-            node.fluid_code.add_layer("unsqueeze",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "unsqueeze", inputs=node, output=node, param_attr=attr)
         if len(shrink_axes) > 0:
             if len(input.out_shapes[0]) + len(new_axes) <= 1:
                 pass
             else:
                 attr = {"axes": shrink_axes}
-                node.fluid_code.add_layer("squeeze",
-                                          inputs=node,
-                                          output=node,
-                                          param_attr=attr)
+                node.fluid_code.add_layer(
+                    "squeeze", inputs=node, output=node, param_attr=attr)
 
     def Slice(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -909,10 +825,8 @@ class TFOpMapperNHWC(OpMapper):
             "ends": size
         }
 
-        node.fluid_code.add_layer("slice",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "slice", inputs=input, output=node, param_attr=attr)
 
     def Conv2DBackpropInput(self, node):
         out_shape = self.graph.get_node(node.layer.input[0], copy=True)
@@ -950,10 +864,8 @@ class TFOpMapperNHWC(OpMapper):
             strides = [strides[i] for i in [0, 3, 1, 2]]
             dilations = [dilations[i] for i in [0, 3, 1, 2]]
             attr = {"perm": [0, 3, 1, 2]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=input,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=input, output=node, param_attr=attr)
             input = node
         else:
             self.data_format_propagation(node)
@@ -968,17 +880,13 @@ class TFOpMapperNHWC(OpMapper):
             "padding": string(pad_mode),
             "output_size": out_shape[1:3]
         }
-        node.fluid_code.add_layer("conv2d_transpose",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "conv2d_transpose", inputs=input, output=node, param_attr=attr)
 
         if not channel_first:
             attr = {"perm": [0, 2, 3, 1]}
-            node.fluid_code.add_layer("transpose",
-                                      inputs=node,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "transpose", inputs=node, output=node, param_attr=attr)
 
     def Max(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -988,10 +896,8 @@ class TFOpMapperNHWC(OpMapper):
         dim = reduce_idx.value.tolist()
 
         attr = {"dim": dim, "keep_dim": keep_dims}
-        node.fluid_code.add_layer("reduce_max",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "reduce_max", inputs=input, output=node, param_attr=attr)
 
     def Sum(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1001,19 +907,15 @@ class TFOpMapperNHWC(OpMapper):
         dim = reduce_idx.value.tolist()
 
         attr = {"dim": dim, "keep_dim": keep_dims}
-        node.fluid_code.add_layer("reduce_sum",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "reduce_sum", inputs=input, output=node, param_attr=attr)
 
     def Cast(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         dtype = node.dtype_map[node.get_attr('DstT')]
         attr = {"dtype": string(dtype)}
-        node.fluid_code.add_layer("cast",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "cast", inputs=input, output=node, param_attr=attr)
 
     def Split(self, node):
         dim = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1024,28 +926,22 @@ class TFOpMapperNHWC(OpMapper):
         dim = dim.value
 
         attr = {"num_or_sections": num_split, "dim": dim}
-        node.fluid_code.add_layer("split",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "split", inputs=input, output=node, param_attr=attr)
 
     def Squeeze(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         squeeze_dims = node.get_attr('squeeze_dims')
         attr = {"axes": squeeze_dims}
-        node.fluid_code.add_layer("squeeze",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "squeeze", inputs=input, output=node, param_attr=attr)
 
     def Softmax(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         axis = node.get_attr("axis")
         attr = {"axis": axis}
-        node.fluid_code.add_layer("softmax",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "softmax", inputs=input, output=node, param_attr=attr)
 
     def ResizeNearestNeighbor(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1054,24 +950,18 @@ class TFOpMapperNHWC(OpMapper):
         if resize_shape.layer_type == "Const":
             resize_shape = resize_shape.value.tolist()
         else:
-            resize_shape = self.decoder.infer_shape_tensor(
-                resize_shape, node.out_shapes[0])
+            resize_shape = self.decoder.infer_shape_tensor(resize_shape,
+                                                           node.out_shapes[0])
         align_corners = node.get_attr("align_corners")
         attr = {"perm": [0, 3, 1, 2]}
-        node.fluid_code.add_layer("transpose",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "transpose", inputs=input, output=node, param_attr=attr)
         attr = {"align_corners": align_corners, "out_shape": resize_shape}
-        node.fluid_code.add_layer("resize_nearest",
-                                  inputs=node,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "resize_nearest", inputs=node, output=node, param_attr=attr)
         attr = {"perm": [0, 2, 3, 1]}
-        node.fluid_code.add_layer("transpose",
-                                  inputs=node,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "transpose", inputs=node, output=node, param_attr=attr)
 
     def ResizeBilinear(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1080,37 +970,29 @@ class TFOpMapperNHWC(OpMapper):
         if resize_shape.layer_type == "Const":
             resize_shape = resize_shape.value.tolist()
         else:
-            resize_shape = self.decoder.infer_shape_tensor(
-                resize_shape, node.out_shapes[0])
+            resize_shape = self.decoder.infer_shape_tensor(resize_shape,
+                                                           node.out_shapes[0])
         align_corners = node.get_attr("align_corners")
         attr = {"perm": [0, 3, 1, 2]}
-        node.fluid_code.add_layer("transpose",
-                                  inputs=input,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "transpose", inputs=input, output=node, param_attr=attr)
         attr = {
             "align_corners": align_corners,
             "out_shape": resize_shape,
             "align_mode": 1
         }
-        node.fluid_code.add_layer("resize_bilinear",
-                                  inputs=node,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "resize_bilinear", inputs=node, output=node, param_attr=attr)
         attr = {"perm": [0, 2, 3, 1]}
-        node.fluid_code.add_layer("transpose",
-                                  inputs=node,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "transpose", inputs=node, output=node, param_attr=attr)
 
     def GreaterEqual(self, node):
         x = self.graph.get_node(node.layer.input[0], copy=True)
         y = self.graph.get_node(node.layer.input[1], copy=True)
         inputs = {"x": x, "y": y}
-        node.fluid_code.add_layer("greater_equal",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=None)
+        node.fluid_code.add_layer(
+            "greater_equal", inputs=inputs, output=node, param_attr=None)
 
     def RandomUniform(self, node):
         shape = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1123,29 +1005,24 @@ class TFOpMapperNHWC(OpMapper):
 
         if shape[0] < 0:
             input = self.batch_node
-            node.fluid_code.add_layer("uniform_random_batch_size_like",
+            node.fluid_code.add_layer(
+                "uniform_random_batch_size_like",
                 inputs=input,
                 output=node,
                 param_attr=attr)
         else:
-            node.fluid_code.add_layer("uniform_random",
-                                      inputs=None,
-                                      output=node,
-                                      param_attr=attr)
+            node.fluid_code.add_layer(
+                "uniform_random", inputs=None, output=node, param_attr=attr)
 
     def SquaredDifference(self, node):
         x = self.graph.get_node(node.layer.input[0], copy=True)
         y = self.graph.get_node(node.layer.input[1], copy=True)
         inputs = {"x": x, "y": y}
-        node.fluid_code.add_layer("elementwise_sub",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=None)
+        node.fluid_code.add_layer(
+            "elementwise_sub", inputs=inputs, output=node, param_attr=None)
         inputs = {"x": node, "y": node}
-        node.fluid_code.add_layer("elementwise_mul",
-                                  inputs=inputs,
-                                  output=node,
-                                  param_attr=None)
+        node.fluid_code.add_layer(
+            "elementwise_mul", inputs=inputs, output=node, param_attr=None)
 
     def ExpandDims(self, node):
         x = self.graph.get_node(node.layer.input[0], copy=True)
@@ -1156,19 +1033,15 @@ class TFOpMapperNHWC(OpMapper):
             dim = self.decoder.infer_tensor(y)
         self.add_omit_nodes(y.layer_name, node.layer_name)
         attr = {'axes': [dim]}
-        node.fluid_code.add_layer("unsqueeze",
-                                  inputs=x,
-                                  output=node,
-                                  param_attr=attr)
+        node.fluid_code.add_layer(
+            "unsqueeze", inputs=x, output=node, param_attr=attr)
 
     def BatchToSpaceND(self, node):
         x = self.graph.get_node(node.layer.input[0], copy=True)
         y = self.graph.get_node(node.layer.input[1], copy=True)
         if hasattr(node, 'skip') and node.skip:
-            node.fluid_code.add_layer("=",
-                                      inputs=x,
-                                      output=node,
-                                      param_attr=None)
+            node.fluid_code.add_layer(
+                "=", inputs=x, output=node, param_attr=None)
         else:
             raise Exception("BatchToSpaceND is not supported")
 
@@ -1176,9 +1049,7 @@ class TFOpMapperNHWC(OpMapper):
         x = self.graph.get_node(node.layer.input[0], copy=True)
         y = self.graph.get_node(node.layer.input[1], copy=True)
         if hasattr(node, 'skip') and node.skip:
-            node.fluid_code.add_layer("=",
-                                      inputs=x,
-                                      output=node,
-                                      param_attr=None)
+            node.fluid_code.add_layer(
+                "=", inputs=x, output=node, param_attr=None)
         else:
             raise Exception("SpaceToBatchND is not supported")

x2paddle/optimizer/caffe_optimizer.py

@@ -41,7 +41,8 @@ class CaffeOptimizer(object):
                     if is_delete_node:
                         parent_node.fluid_code.clear()
                     node.fluid_code.clear()
-                    node.fluid_code.add_layer("batch_norm",
+                    node.fluid_code.add_layer(
+                        "batch_norm",
                         inputs=input,
                         output=node,
                         param_attr=parent_param_attr)
@@ -62,7 +63,8 @@ class CaffeOptimizer(object):
                     if is_delete_node:
                         parent_node.fluid_code.clear()
                     node.fluid_code.clear()
-                    node.fluid_code.add_layer(op,
+                    node.fluid_code.add_layer(
+                        op,
                         inputs=input,
                         output=node,
                         param_attr=parent_param_attr)

x2paddle/optimizer/tf_optimizer.py

@@ -554,7 +554,8 @@ class TFOptimizer(object):
                 node.fluid_code.layers[0].param_attr["shape"] = shape
                 node.fluid_code.layers[0].output = "nhwc_" + name
                 attr = {"perm": [0, 2, 3, 1]}
-                node.fluid_code.add_layer("transpose",
+                node.fluid_code.add_layer(
+                    "transpose",
                     inputs="nhwc_" + name,
                     output=node,
                     param_attr=attr)
@@ -767,8 +768,8 @@ class TFOptimizer(object):
                     is_prelu = False
                     continue
 
-                if len(in_nodes0[0].outputs) != 1 or len(
-                        in_nodes0[1].outputs) != 1:
+                if len(in_nodes0[0].outputs) != 1 or len(in_nodes0[1]
+                                                         .outputs) != 1:
                     is_prelu = False
                     continue
 
@@ -777,8 +778,8 @@ class TFOptimizer(object):
                     self.graph.get_node(in_name)
                     for in_name in in_nodes0[1].inputs
                 ]
-                if in_nodes2[1].layer_type != "Const" or numpy.fabs(
-                        in_nodes2[1].value - 0.5) > 1e-06:
+                if in_nodes2[1].layer_type != "Const" or numpy.fabs(in_nodes2[
+                        1].value - 0.5) > 1e-06:
                     is_prelu = False
                     continue
                 if in_nodes2[0].layer_type != "Mul":
@@ -787,8 +788,8 @@ class TFOptimizer(object):
                 if exist_act(in_nodes2[0]):
                     is_prelu = False
                     continue
-                if len(in_nodes2[1].outputs) != 1 or len(
-                        in_nodes2[0].outputs) != 1:
+                if len(in_nodes2[1].outputs) != 1 or len(in_nodes2[0]
+                                                         .outputs) != 1:
                     is_prelu = False
                     continue
 
@@ -803,8 +804,8 @@ class TFOptimizer(object):
                 if exist_act(in_nodes3[1]):
                     is_prelu = False
                     continue
-                if len(in_nodes3[0].outputs) != 1 or len(
-                        in_nodes3[1].outputs) != 1:
+                if len(in_nodes3[0].outputs) != 1 or len(in_nodes3[1]
+                                                         .outputs) != 1:
                     is_prelu = False
                     continue
 
@@ -856,12 +857,12 @@ class TFOptimizer(object):
                     mode = "element"
                 elif len(in_nodes3[0].value.shape) == 0:
                     mode = "all"
-                elif len(in_nodes3[0].value.shape
-                         ) == 1 and in_nodes3[0].value.shape[0] == 1:
+                elif len(in_nodes3[0].value.shape) == 1 and in_nodes3[
+                        0].value.shape[0] == 1:
                     mode = "all"
-                elif len(in_shape) == 4 and len(
-                        in_nodes3[0].value.shape
-                ) == 1 and in_nodes3[0].value.shape[0] == in_shape[-1]:
+                elif len(in_shape) == 4 and len(in_nodes3[
+                        0].value.shape) == 1 and in_nodes3[0].value.shape[
+                            0] == in_shape[-1]:
                     mode = "channel"
                     weight = self.op_mapper.weights[in_nodes3[0].layer_name]
                     weight = numpy.expand_dims(weight, 0)
@@ -916,14 +917,15 @@ class TFOptimizer(object):
                     self.graph.get_node(in_name) for in_name in node.inputs
                 ]
                 if in_nodes0[0].layer_type != "Mul" or in_nodes0[
-                        1].layer_type != "Const" or in_nodes0[1].value.size != 1:
+                        1].layer_type != "Const" or in_nodes0[
+                            1].value.size != 1:
                     is_scale = False
                     continue
                 if exist_act(in_nodes0[0]):
                     is_scale = False
                     continue
-                if len(in_nodes0[0].outputs) != 1 or len(
-                        in_nodes0[1].outputs) != 1:
+                if len(in_nodes0[0].outputs) != 1 or len(in_nodes0[1]
+                                                         .outputs) != 1:
                     is_scale = False
                     continue
 
@@ -939,8 +941,8 @@ class TFOptimizer(object):
                 if exist_act(in_nodes1[1]):
                     is_scale = False
                     continue
-                if len(in_nodes1[0].outputs) != 1 or len(
-                        in_nodes1[1].outputs) != 1:
+                if len(in_nodes1[0].outputs) != 1 or len(in_nodes1[1]
+                                                         .outputs) != 1:
                     is_scale = False
                     continue
 
@@ -962,8 +964,8 @@ class TFOptimizer(object):
                     scale = 1.0 / in_nodes2[1].value * in_nodes1[0].value
                     act = None
                     if node.fluid_code.layers[0].param_attr is not None:
-                        act = node.fluid_code.layers[0].param_attr.get(
-                            "act", None)
+                        act = node.fluid_code.layers[0].param_attr.get("act",
+                                                                       None)
                     node.fluid_code.clear()
 
                     attr = {
@@ -972,10 +974,8 @@ class TFOptimizer(object):
                         "bias_after_scale": True,
                         "act": act
                     }
-                    node.fluid_code.add_layer("scale",
-                                              inputs=in_node,
-                                              output=node,
-                                              param_attr=attr)
+                    node.fluid_code.add_layer(
+                        "scale", inputs=in_node, output=node, param_attr=attr)
 
                     del self.graph.node_map[in_nodes0[0].layer_name]
                     del self.graph.node_map[in_nodes0[1].layer_name]
@@ -1004,17 +1004,17 @@ class TFOptimizer(object):
                 if exist_act(in_nodes0[0]):
                     is_affine_channel = False
                     continue
-                if len(in_nodes0[0].outputs) != 1 or len(
-                        in_nodes0[1].outputs) != 1:
+                if len(in_nodes0[0].outputs) != 1 or len(in_nodes0[1]
+                                                         .outputs) != 1:
                     is_affine_channel = False
                     continue
                 in_nodes1 = [
                     self.graph.get_node(in_name)
                     for in_name in in_nodes0[0].inputs
                 ]
-                if len(in_nodes1[0].out_shapes[0]
-                       ) != 4 or in_nodes1[1].layer_type != "Const" or len(
-                           in_nodes1[1].value.shape) != 3:
+                if len(in_nodes1[0].out_shapes[0]) != 4 or in_nodes1[
+                        1].layer_type != "Const" or len(in_nodes1[1]
+                                                        .value.shape) != 3:
                     is_affine_channel = False
                     continue
                 if len(in_nodes1[1].outputs) != 1:
@@ -1037,8 +1037,8 @@ class TFOptimizer(object):
                     node.layer_type = "AffineChannel"
                     node.inputs = [in_node.layer_name]
                     scale = 1.0 / in_nodes0[1].value.flatten()
-                    bias = in_nodes1[1].value.flatten(
-                    ) / in_nodes0[1].value.flatten()
+                    bias = in_nodes1[1].value.flatten() / in_nodes0[
+                        1].value.flatten()
                     if not bias_add:
                         bias *= -1.0
                     self.op_mapper.weights[node.layer_name + "_scale"] = scale
@@ -1046,8 +1046,8 @@ class TFOptimizer(object):
 
                     act = None
                     if node.fluid_code.layers[0].param_attr is not None:
-                        act = node.fluid_code.layers[0].param_attr.get(
-                            "act", None)
+                        act = node.fluid_code.layers[0].param_attr.get("act",
+                                                                       None)
                     node.fluid_code.clear()
 
                     attr = {
@@ -1055,7 +1055,8 @@ class TFOptimizer(object):
                         "shape": [channel],
                         "name": string(node.layer_name + "_scale")
                     }
-                    node.fluid_code.add_layer("create_parameter",
+                    node.fluid_code.add_layer(
+                        "create_parameter",
                         inputs=None,
                         output=node.layer_name + "_scale",
                         param_attr=attr)
@@ -1064,7 +1065,8 @@ class TFOptimizer(object):
                         "shape": [channel],
                         "name": string(node.layer_name + "_bias")
                     }
-                    node.fluid_code.add_layer("create_parameter",
+                    node.fluid_code.add_layer(
+                        "create_parameter",
                         inputs=None,
                         output=node.layer_name + "_bias",
                         param_attr=attr)
@@ -1074,7 +1076,8 @@ class TFOptimizer(object):
                         "bias": node.layer_name + "_bias"
                     }
                     attr = {"act": act}
-                    node.fluid_code.add_layer("affine_channel",
+                    node.fluid_code.add_layer(
+                        "affine_channel",
                         inputs=inputs,
                         output=node,
                         param_attr=attr)

x2paddle_model_zoo.md

@@ -13,6 +13,7 @@
 | ShuffleNet | [code](https://github.com/TropComplique/shufflenet-v2-tensorflow) |-|
 | mNASNet | [code](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) |-|
 | EfficientNet | [code](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |-|
+| Inception_V3 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py) |-|
 | Inception_V4 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py) |-|
 | Inception_ResNet_V2 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py) |-|
 | VGG16 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|